nature essay on sanskrit

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पर्यावरण पर संस्कृत में निबंध। Essay on Environment in Sanskrit

पर्यावरण पर संस्कृत में निबंध। Essay on Environment in Sanskrit : वयं वायुजलमृदाभिः आवृत्ते वातावरणे निवसामः। एतदेव वातावरणं पर्यावरण कथ्यते। पर्यावरणेनैव वयं जीवनोपयोगिवस्तुनि प्राप्नुमः। जलं वायुः च जीवने महत्वपूर्णो स्तः। साम्प्रतं शुद्ध - पेय - जलस्य समस्या वर्तते। अधुना वायुरपि शुद्धं नास्ति। एवमेव प्रदूषित-पर्यावरणेन विविधाः रोगाः जायन्ते। पर्यावरणस्य रक्षायाः अति आवश्यकता वर्तते।

पर्यावरण पर संस्कृत में निबंध। E ssay on Environment in Sanskrit

I need essay on swach bharat

Bhai tree Ka nibandh chaiye Sanskrit me

http://www.hindivyakran.com/2018/09/essay-on-tree-in-sanskrit.html

I too wanted on the same 😢😢😢

may i get essay on atmosphere in Sanskrit language with detail. thank you.

निबंध दोबारा देखें. इसे अपडेट कर दिया गया है.

Ye nibhand pura nahi hai aur galtiya hai

I am a Sanskrit teacher and in essay there is lots of mistakes and it is incomplete please complete it 😠😠😡😡

https://www.hindivyakran.com/2017/12/essay-on-environment-in-sanskrit.html

Sir pls hindi essay ki pdf send kr skte hai kya

👌 अच्छा है पर पर्यावरण वर मराठी निबंध जाणून घ्या

environment essay in hindi

Very very important information sir thanks for sharing such a great information nice information

हिंदी ब्लॉग सम्बंधित आपकी बताई गई जानकारी हमे बहुत पसंद आई, शेयर करने के लिए आपका धन्यवाद Pollution in Hindi

thank you so much you can read also my blog perfect taiyari

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गम् धातु के रूप संस्कृत में – Gam Dhatu Roop In Sanskrit गम् धातु के रूप संस्कृत में – Gam Dhatu Roop In Sanskrit यहां पढ़ें गम् धातु रूप के पांचो लकार संस्कृत भाषा में। गम् धातु का अर्थ होता है जा...
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पर्यावरणम् | Environment Essay in Sanskrit

“स्वल्यं तथायुर्बहुवश्च विनाः”

कलियुगे मानवाः दीर्घजीविनो न भवन्ति । केचित् व्याधिपीडिताः, केचित् क्षुधा ताडिताः, केचित् दरिद्रता दूषिताः, केचित् पर्यावरण-भक्षिताः जनाः अल्पायुषाः भवन्ति प्रतिदिनम् यमलोकम् प्रयान्ति च । तेषु अन्येषु च कारणेषु प्रदूषितम पर्यावरणम् अत्यधिकम् जीवन घातकम् वर्तते । अनेन मानव-शरीरे शनैः-शनैः विषारोपणम् क्रियते । अन्ते च अंगहीनो भूत्वा, अन्धो भूत्वा हृद्-रोगी भूत्वा च मृत्योः शरणम् गच्छति । ।

प्रकृति; अस्माकम् संरक्षिका अस्ति । अभ्रंकषाः पर्वताः, मर्यादिताः गम्भीराः सागराः, सघनानि वनानि, सोत्पलानि सरांसि, नर्तनपरा: नराः, अस्माकम् जविन-वृक्षम् सम्वर्धयन्ति । हर्षिताः तरः प्रसन्ना लतः वायोः विकारान् स्वयम् पिबन्ति अस्मभ्यम् स्वास्थ्य प्रदम् वायुमण्डलम् प्रयच्छन्ति । वृक्षाः नाना प्रकाराणि फलानि, पुष्पाणि च दत्त्वा अस्मान् बहु उपकुर्वन्ति । वातावरणस्य वायोश्च परिशोधनम् कुर्वन्ति । वनानि एवं पर्यावरणस्य सन्तुलनम् स्थापयन्ति ।।

साम्प्रतम् वयम् प्रकृतेः दूरातूदूरतः जातः । को कथा वनानाम्, इदानीम्। तु पत्राणि, पुष्पाणि, फलानि अपि न दृश्यन्ते । वनानि छिन्द्यन्ते, फलाच्छादितानि उद्यानानि उच्छिद्य नवानि भवनानि, नगराणि, फैक्ट्री इति नामधेयानि यन्त्रागाराणि निर्मीयन्ते । नगराणि महानगराणि जातानि । कृषियोग्यानि क्षेत्राणि विनष्टीकृत्य हट्टानि विनिर्मीयन्ते ।।

भौतिकवादी युगे इदानीम् ‘डीजल’ पैट्रोल इत्यादि नामकैः तैलैः चालितानि यानानि, राजमार्गान् धूम्रमयान् कुर्वन् इतस्तत: धावन्ति गगने । वायुयानानि उड्डीयन्ते तेषु निर्गता घुमशिखाऽपि घातका एव । विभिन्नेषु उद्योग-संस्थानेषु यन्त्राणि अहर्निशं धूमायन्ति वायुमण्डलम् । अधुना तु स्वास्थ्य हानिकर वायु प्रदूषणम् प्रतिक्षणम् वर्धते एव ।।

पर्वतानाम् शिलाखण्डानि विदार्य वनानि विच्छिद्य राजमार्गाणि विनिमयन्ते । तेषु गिरि मागेषु दीघनि यानानि धावन्ति । नाना प्रकाराणाम् अखाणाम् परीक्षणैः समुद्रस्य वातावरणम् अपि प्रदूष्यते । पर्वतानाम् वनसंरक्षणे चिपको आन्दोलनस्य जनकः श्री सुन्दरलाल बहुगुणा महोदयः भूरि-भूरि प्रशंसनीयः येन स्वकीये आन्दोलने समागतानि कष्टानि अविगणय्य पर्वतीयानि वनानि, वृक्षाणि च संरक्षितानि ।।

यथा प्रदूषण-विस्तारकाणि यन्त्राणि यानानि च परिवर्धन्ते तथा प्रदूषण-परिशोधन साधनानि न वर्धन्ते । धन-लाभाय वनानि छिन्यन्ते । मनोरंजनाय

Also Read: Essay on Durga Puja Essay on Environment Essay on Ganga River Essay on Himalaya Mountain Essay on Ideal Citizen Essay on Mahabharata

2 thoughts on “पर्यावरणम् | Environment Essay in Sanskrit”

Essay but many words are very tough words

ok. Will try to post another copy of this essay in simpler words. 🙂

World Environment Day in Sanskrit

June 4, 2024

Sanskrit and its literature have always respected and celebrated the environment. Take this Shloka from Atharvaveda as an example; it establishes a sacred connection between mother nature and us (humans), setting a very deep precedent for how we should be treating our fellow earthlings. Happy Environment Day to you!

world environment day sanskrit post 2024

प्रकृतिरेव शरणम्।

संरक्षेद्दूषितो न स्याल्लोकः मानवजीवनम्।

माता भूमिः पुत्रोऽहं पृथिव्याः।

Bring Sanskrit home! ReSanskrit merchandise starting at ₹ 199.

शैले शैले न माणिक्यं मौतिकं न गजे गजे । साधवो नहि सर्वत्र चन्दनं न वने वने ॥

Read next - Relevant Sanskrit Shlokas with Meaning in Hindi & English

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Very nice post

plese send me all Sanskrit masseges to my Email to read every day

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The Past Teaching the Present: Ancient Sanskrit Texts Discuss the Importance of Environmental and Species Conservation

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One of the greatest challenges facing humanity in the 21st century is the destruction of the natural environment. Researchers have found that environmental change over the last 60 years is happening at a rate unseen in the past 10 000 years. Human-driven climate change, the loss of biosphere integrity, land system change, and the high amounts of biochemicals flowing into oceans due to fertilizer use are said to have reached an unsafe level. With these problems in mind, environmental conservation has become a hot topic in modern society. Nevertheless, this concept has been around for a much longer period of time, and can even be found in Sanskrit texts from ancient India.

The Environment and Human Connection

Lessons about environmental conservation can be found within the teachings of Hinduism. Adherents of this religion believe, for instance, that the environment is made up of five great elements – space, air, fire, water, and earth. The human body is also composed of and is related to these elements. Additionally, each of the five senses is connected to one of the five elements. The link between the senses and the elements forms the basis for the bond between human beings and the natural world. Therefore, in the teachings of the Hindu faith, it is believed that the environment is not an external entity, but an intrinsic, inseparable part of human existence, as they constitute the human body.

The five elements of nature and the human body (earth, air, fire, water and ether/space) interconnect according to the Hindu faith. ( Economy.rs )

Dharma and Environmental Conservation

With this belief in mind, one may better understand the idea of protecting the environment as part of Dharma . The word Dharma has been translated as ‘duty’, ‘virtue’, ‘cosmic order’, and even ‘religion’. It has been pointed out that in the past, Indian communities did not view religion, ethics, and the environment as separate aspects of life, instead there was interconnectedness between the elements - much like the way they viewed the relationship between human beings and the natural world. For example, the Bishnois protected animals and trees, the Swadhyayis built Vrikshamandiras (tree temples) and Nirmal Nirs (water harvesting sites) and the Bhils practiced their rituals in sacred groves. Rather than seeing their actions as ‘restoring the environment’, these communities understood that they were expressing reverence for the environment in accordance with the teachings of Hinduism.

Illustration of the Khejarli Massacre (1730) in which 363 Bishnol men, women, and children were killed while trying to protect trees from being cut down. ( Wikimedia Commons )

The Code of Manu Samhita: Protection of Fauna and Flora

A more ‘active’ form of environmental conservation may be found in an important Sanskrit code of law known as the Manu Samhita . It is stated in the Manu Samhita that the protection of animals is one of the duties of a king. In the text, various offences against animals and the respective punishments are also listed. For example, if a person wounds an animal, the offender would be required to bear the cost of the treatment. If a noble animal (e.g. a cow, an elephant, or a horse) is harmed, a fine would be imposed on the offender. Furthermore, protection is given to many animals that are sacred in Hinduism and the killing of certain animals, including cats, snakes, monkeys and various birds, is a sin, and is punishable. Protection is extended to plant life as well. As an example, the punishment for felling live trees for the construction of factories, dams, bridges, etc., or for the purpose of obtaining firewood is the condemnation of the offender as a degraded person.

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The ancient Hindu belief holds cows as symbols of abundance, power, and altruistic giving. ( Himalayan Academy/Wikimedia Commons )

Reincarnation and the Unity of the Animal Kingdom

Hinduism, however, is not the only religion originating in India that promotes environmental conservation. This concept can also be found in the teachings of Buddhism. For example, the Sanskrit Jatakamala is a collection of tales regarding the past lives of the Buddha. Of the 34 tales, the Buddha is reincarnated as an animal, a bird, or a fish a total of 14 times. As this belief in reincarnation suggests that human beings may be reborn as animals and vice versa , the Jatakamala reminds its readers that there is an inherent unity and continuity between the human beings and the animal kingdom. Thus, the message of respecting and revering the environment is once more echoed.

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New study blames humans for megafauna extinction

Bhutanese painting of the Jataka Tales, showing reincarnation. Phajoding Gonpa, Thimphu, Bhutan ( Wikimedia Commons )

Using Ancient Teachings in Today’s World

These are some of the messages passed down from the ancient Indians regarding environmental and species conservation. Their message is one that human beings today ought to pay attention to. By viewing the natural world as an inseparable part of human existence, we may learn to treat it with more respect and reverence, and therefore begin to better protect it, rather than exploiting it to satisfy humanity’s seemingly insatiable desires.

Featured image: “Krishna and Balarama Taking the Cattle to Graze” from a Bhagavata Purana Manuscript (1520-1540), Museum Rietberg, Zurich ( Wikimedia Commons )

Down To Earth, 2015. `Ancient Indian literature displays exact knowledge of environmental phenomena'. [Online] Available at: http://www.downtoearth.org.in/interviews/ancient-indian-literature-displays-exact-knowledge-of-environmental-phenomena-49232

Jain, P., 2010. Ten Key Hindu Environmental Teachings. [Online] Available at: http://www.greenfaith.org/files/top-10-hindu-teachings-handout

Milman, O., 2015. Rate of environmental degradation puts life on Earth at risk, say scientists. [Online] Available at: http://www.theguardian.com/environment/2015/jan/15/rate-of-environmental-degradation-puts-life-on-earth-at-risk-say-scientists

Nair, S. M., 2015. Cultural Traditions of Nature Conservation in India. [Online] Available at: http://ccrtindia.gov.in/readingroom/nscd/ch/ch11.php

Sensarma, P., 1998. Conservation of Biodiversity in Manu-Samhita. [Online] Available at: http://www.dli.gov.in/rawdataupload/upload/insa/INSA_2/20005a60_267.pdf

About this western phenomenon of protecting trees, it's basically the media applying Occam's razor principle; https://en.m.wikipedia.org/wiki/Occam 's_razor

In doing so, by boiling down an acknowledgement that the whole biosphere is under threat, in so many ways, they use trees as a pinnacle example so then 'resource exploitation' and business can carry on uninterrupted, government's can be seen as 'doing something to avert disaster' and the plebs can drive past a few trees that got planted in awe, although they don't really promote biodiversity at all, trees themselves don't make an ecosystem. Hope it makes sense.

The protection of trees is peculiar anomaly in religious practices. Off the top of my head there aren't any equivalents to this practice in Greece, Persia, or the Near East (perhaps in old Greek rustic religions relating to Pan, and Canaanite Asherah worship). Seems strange considering many Indo European religions have a mother earth goddess figure within them

Wu Mingren (‘Dhwty’) has a Bachelor of Arts in Ancient History and Archaeology. Although his primary interest is in the ancient civilizations of the Near East, he is also interested in other geographical regions, as well as other time periods.... Read More

SANSKRIT BHUVAN

Nature related words in sanskrit to english.

Sanskrit English Transliteration नदीशय्या Bed of the river Nadīśyyā शाखानदी Branch river Śākhānadī शैत्यम् Cold Śaityaṁ प्रकृतिः Climate Prakr̥tiḥ धूमकेतुः Comet Dhūmaketuḥ स्रोतः Current Srotaḥ चक्रवातः Cyclone Chakravātaḥ

Nature related words in Sanskrit to English

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nature related Sanskrit words, Sanskrit word for nature, Sanskrit word for environment, nature related Sanskrit words, Sanskrit word for natural

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An Essay On The Nature, Age, And Origin Of The Sanskrit Writing And Language (1838) Paperback – January 26, 2009

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Print length 92 pages
Language English
Publisher Kessinger Publishing
Publication date January 26, 2009
Dimensions 8.25 x 0.19 x 11 inches
ISBN-10 1104014513
ISBN-13 978-1104014513
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Product details

Publisher ‏ : ‎ Kessinger Publishing (January 26, 2009)
Language ‏ : ‎ English
Paperback ‏ : ‎ 92 pages
ISBN-10 ‏ : ‎ 1104014513
ISBN-13 ‏ : ‎ 978-1104014513
Item Weight ‏ : ‎ 8.1 ounces
Dimensions ‏ : ‎ 8.25 x 0.19 x 11 inches

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प्रकृति पर संस्कृत श्लोक हिंदी अर्थ सहित

Sanskrit Shlokas on Nature with Hindi Meaning

प्रकृति पर संस्कृत श्लोक हिंदी अर्थ सहित | Sanskrit Shlokas on Nature with Hindi Meaning

मधु॒ वाता ऋतायते मधु क्षरन्ति सिन्धवः। माध्वीर्नः सन्त्वोषधीः।। भावार्थ: हे विद्वान् पूर्ण ज्ञानी! आप और वह व्यक्ति जो हवा की मिठास और समुद्र या नदियों के साथ-साथ कोमलता आदि के मीठे गुणों को डालना चाहते हैं, हमें मीठे गुणों के बारे में विशेष ज्ञान देना चाहिए।

शाश्वतम्, प्रकृति-मानव-सङ्गतम्, सङ्गतं खलु शाश्वतम्। तत्त्व-सर्वं धारकं सत्त्व-पालन-कारकं वारि-वायु-व्योम-वह्नि-ज्या-गतम्। शाश्वतम्, प्रकृति-मानव-सङ्गतम्।।(ध्रुवम्) भावार्थ: प्रकृति और मनुष्य के बीच का संबंध शाश्वत है। रिश्ता शाश्वत है। जल, वायु, आकाश के सभी तत्व, अग्नि और पृथ्वी वास्तव में धारक हैं और जीवों के पालनहार।

दश कूप समा वापी, दशवापी समोह्नद्रः। दशह्नद समः पुत्रों, दशपुत्रो समो द्रमुः।। भावार्थ: एक पेड़ दस कुओं के बराबर,एक तालाब दस सीढ़ी के कुएं के बराबर, एक बेटा दस तालाब के बराबर, एक पेड़ दस बेटों के बराबर।

Sanskrit Shlokas on Nature with Hindi meaning

सन्ति निरतं जीव-जगतां प्राण-दाने, तरु-लतानां विविध-वर्गाः शं दधाने। वन-गिरि-नदी-पशु-विहङ्गाः रात्रि-दिन-ऋतु-शशि-पतङ्गाः, सर्वमास्ते जन-हितार्थं संहतम्। रक्षति प्रकृतिः सती सौख्य-राशिं तन्वती वन्य-सम्पद् रक्षणीया सन्ततम्। शाश्वतम्, प्रकृति-मानव-सङ्गतम्।। रिश्ता शाश्वत है भावार्थ: विभिन्न प्रकार के पेड़ और लता हमेशा जीवन देने में व्यस्त और कल्याण की पेशकश करने वाले मामलों में चेतन प्राणियों की दुनिया के लिए। जंगल, पहाड़, नदियाँ, पशु और पक्षी, अगला रातें, दिन, ऋतुएँ, चाँद और सूरज, सब एक साथ लगे लोगों की भलाई के लिए। प्रकृति अच्छी तरह से रक्षा करती है और सभी प्रकार के सुखों को प्रदान करता है। तो सभी प्राणी जो धन हैं वन क्षेत्र होना चाहिए हमेशा ठीक से संरक्षित। रिश्ता शाश्वत है प्रकृति और मनुष्य के बीच।

यस्ताडागं नवं कुर्पात् पुल्णं वापि खानयेत्। स सर्वं कुलमुद्धृत्य स्वर्ग लोके महीयते।। भावार्थ: जो व्यक्ति पुराने बावड़ी में तालाब और बगीचों की खुदाई करता है या उसे नए सिरे से बनवाता है, उसे नए जल निकायों के निर्माण और नए बाग लगाने का फल मिलता है।

भातु पर्यावरणममलं दिग्‌विताने, यातु हिंसा ध्वंसमचिरं सन्निधाने। अवतु पवनो मुक्त-गगनं स्वच्छ-परिमल-धौत-सदनं दुर्नयानां पर्व यातु पराहतम्। प्रीति-मैत्री-बोधना हार्दिकी सद्‍भावना हन्तु सर्वं वैर-वर्वर-पर्वतम्। शाश्वतम्, प्रकृति-मानव-सङ्गतम्।। भावार्थ: पर्यावरण साफ चमकता है और सभी दिशाओं में प्रदूषण मुक्त। जल्द ही हिंसा का सफाया हो सकता है नेक कार्यों की उपस्थिति में। हवा खुली फर्म की रक्षा कर सकती है और घरों में फैल गया स्वच्छता और सुगंध के साथ। भ्रष्टाचार का सिलसिला पराजित और नष्ट किया जा सकता है। गर्म साथी भावनाएं स्नेह और मित्रता से भरपूर क्रूरता और शत्रुता के पहाड़ को नष्ट करो। रिश्ता शाश्वत है प्रकृति और मनुष्य के बीच।

तुलस्याः पल्लवं विष्णोः शिरस्यारोपितं कलौ। आरोपयति सर्वाणि श्रेयांसि वरमस्तके।। भावार्थ: कलियुग में श्री विष्णु के मस्तक पर तुलसी के पत्ते चढ़ाए जाते थे, अर्थात भगवान द्वारा भक्त के लिए तुलसी सर्वोत्तम वरदान है।

यह भी पढ़े: परिश्रम पर संस्कृत में श्लोक

शान्ति-मन्त्रो जयतु नितरां सौम्य-गाने, प्रेम-गङ्गा वहतु सुजला ऐक्य-ताने। निवसतु सुखं विश्व-जनता लीयतां ननु दनुज-घनता, दिव्य-तेजो भातु भव्यमनारतम्। प्राणिनां संवेदना वर्धतां शुभकामना भातु सत्यं सुन्दरं शिव-सम्मतम्। शाश्वतम्, प्रकृति-मानव-सङ्गतम्।। भावार्थ: शांति का मंत्र जय हो, शांत और मधुर गायन के साथ। प्यार और स्नेह की नदी पवित्र जल से भरा प्रवाह मधुर संगीत सभी के बीच एकता और सद्भाव की। सभी सुखी रहें। आसुरी गुणों का नाश करो। दिव्य महिमा सभी पलों को शानदार ढंग से रोशन किया। करुणामयी भावनाएँ और विकास की हार्दिक शुभकामनाएँ प्राप्त करें। सत्य, सौंदर्य और शुभता हमेशा के लिए चमक रहा है। रिश्ता शाश्वत है प्रकृति और मनुष्य के बीच।

न तेजस्तेजस्वी प्रसृतमपरेषां प्रसहते। स तस्य स्वो भावः प्रकृति नियतत्वादकृतकः।। भावार्थ: शास्त्रों में कहा गया है कि एक प्रतिभाशाली और स्वाभिमानी व्यक्ति किसी और की प्रतिभा को बर्दाश्त और सहन नहीं कर सकता क्योंकि यह उसका स्वभाव से दिया हुआ स्वभाव है।

पश्यैतान् महाभागान् पराबैंकान्तजीवितान्। वातवर्षातपहिमान् सहन्तरे वारयन्ति नः।। भावार्थ: पेड़ इतने महान हैं कि वे केवल दान के लिए जीते हैं। वे तूफान, बारिश और ठंड को अपने आप सहन करते हैं।

यः स्वभावो हि यस्यास्ति स नित्यं दुरतिक्रमः। श्वा यदि क्रियते राजा स किं नाश्नात्युपानहम्।। भावार्थ: जिसका स्वभाव नहीं बदला जा सकता, अगर कुत्ते को राजा बना दिया जाए तो क्या वह जूता नहीं खाएगा?

अहो एषां वरं जन्म सर्वप्राण्युपजीवनम्। सुजनस्यैव येषां वै विमुखा यान्ति नार्थिनः।। भावार्थ: उनका जन्म बहुत अच्छा है क्योंकि उनके कारण ही सभी जीव जीवित हैं। जिस प्रकार सज्जन के सामने कोई याचिकाकर्ता खाली हाथ नहीं जाता, उसी प्रकार इन पेड़ों के पास कोई खाली हाथ नहीं जाता।

अद्रोहः सर्वभूतेषु कर्मणा मनसा गिरा। अनुग्रहश्च दानं च शीलमेतद्विदुर्बुधाः।। भावार्थ: हिंदू शास्त्रों के अनुसार, बुद्धिमान लोगों के अनुसार विनम्रता (नैतिक आचरण और व्यवहार) वाले व्यक्ति का स्वभाव हमेशा कर्म, मन और वचन से सभी प्राणियों के प्रति प्रेम, प्रेम और दान की भावना रखना है।

पुत्रपुष्यफलच्छाया मूलवल्कलदारुभिः। गन्धनिर्यासभस्मास्थितौस्मैः कामान् वितन्वते।। भावार्थ: प्रकृति हमें पत्र, फूल, फल, छाया, जड़, बल्क, लकड़ी और जलाऊ लकड़ी, सुगंध, राख, गुठली और अंकुर प्रदान करके हमारी इच्छाओं को पूरा करते हैं।

व्याघ्रः सेवति काननं च गहनं सिंहो गृहां सेवते हंसः सेवति पद्मिनी कुसुमितां गृधः श्मशानस्थलीम्। साधुः सेवति साधुमेव सततं नीचोऽपि नीचं जनम् या यस्य प्रकृतिः स्वभावजनिता केनापि न त्यज्यते।। भावार्थ: जैसे शेर समृद्ध जंगलों और गुफाओं में रहता है, हंस पानी में खिले फूलों के साथ रहना पसंद करता है। इसी प्रकार साधु को साधु का ही संग अच्छा लगता है और दुष्ट और नीच व्यक्ति को दुष्टों का संग ही अच्छा लगता है। जन्म और बाल्यावस्था से प्राप्त प्रकृति नहीं बदलती।

एतावज्जन्मसाफल्यं देहिनामिह देहिषु। प्राणैरर्थधिया वाचा श्रेय एवाचरेत् सहा।।’ भावार्थ: प्रत्येक जीव का कर्तव्य है कि वह अपने जीवन, धन, बुद्धि और वाणी से दूसरों के कल्याण के लिए कल्याणकारी कार्य करें।

निम्नोन्नतं वक्ष्यति को जलानाम् विचित्रभावं मृगपक्षिणां च। माधुर्यमिक्षौ कटुतां च निम्बे स्वभावतः सर्वमिदं हि सिद्धम्।। भावार्थ: यह प्रकृति अपने आप में एक रहस्य है, जिसने पानी की गहराई और ऊंचाई की शिक्षा दी, जिसने पशु-पक्षियों में विचित्रता, गन्ने में मिठास और नीम में कड़वापन सिखाया, लेकिन यह कहां से आया। ये सभी स्वभाव प्रकृति द्वारा दिए गए हैं, इनमें कोई परिवर्तन नहीं किया जा सकता है।

यह भी पढ़े: परिवार पर संस्कृत श्लोक

ईशा वास्यमिदं सर्वं यत्किञ्च जगत्यां जगत्‌। तेन त्यक्तेन भुञ्जीथा मा गृधः कस्यस्विद्धनम्‌।। भावार्थ: इस लौकिक गति में, इस गतिशील स्थूल-विश्व में जो कुछ भी दृश्यमान गतिशील, व्यक्तिगत दुनिया (दृश्य प्रकृति / वातावरण) है – यह सब भगवान के निवास के लिए है। आपको इसका सेवन त्याग के रूप में करना चाहिए (अर्थात जितना जरूरत हो उतना ही सेवन करें) किसी और की दौलत का लालची मत देखो।

वचो हि सत्यं परमं विभूषणम् यथांगनायाः कृशता कटौ तथा। द्विजस्य विद्यैव पुनस्तथा क्षमा शीलं हि सर्वस्य नरस्य भूषणम्।। भावार्थ: जिस प्रकार पतली कमर स्त्री का रत्न और विद्वान का ज्ञान है, उसी प्रकार सत्य और क्षमा परम वरदान हैं और विनय सभी मनुष्यों का आभूषण है।

अहो एषां वरं जन्म सर्व प्राण्युपजीवनम्। धन्या महीरूहा येभ्यो निराशां यान्ति नार्थिन:।। भावार्थ: सभी प्राणियों का भला करने वाले वृक्षों का जन्म सर्वोत्तम है। धन्य हैं ये पेड़ जिनसे भिखारी कभी निराश होकर नहीं लौटते।

प्रकृति पर संस्कृत श्लोक अर्थ सहित पुष्प-पत्र-फलच्छाया. मूलवल्कलदारुभिः। धन्या महीरुहा येषां. विमुखा यान्ति नार्थिनः।। भावार्थ: धन्य हैं वे वृक्ष, जिनसे फूल, पत्ते, फल, छाया, जड़, छाल और लकड़ी का लाभ उठाकर भिखारी कभी निराश नहीं लौटता।

स्वभावो न उपदेशेन शक्यते कर्तुमन्यथा। सुतप्तमपि पानीयं पुनर्गच्छति शीतताम्।। भावार्थ: हिंदू शास्त्रों में कहा गया है कि केवल उपदेश देने से किसी का स्वभाव नहीं बदला जा सकता, मनुष्य का स्वभाव उसके अनुभव के आधार पर ही बदलता है। जैसे पानी को ज्यादा गर्म करने पर वह गर्म हो जाता है, लेकिन कुछ समय बाद फिर से ठंडा हो जाता है।

काकः पद्मवने रतिं न कुरुते हंसो न कूपोदके मूर्खः पण्डितसंगमे न रमते दासो न सिंहासने। कुस्ती सज्जनसंगमे न रमते नीचं जनं सेवते या यस्य प्रकृतिः स्वभावजनिता केनापि न त्यज्यते।। भावार्थ: कौआ कभी पद्मावन को प्यार नहीं करता, हंस कभी कुएं के पानी को स्वीकार नहीं करता, मूर्ख कभी पुजारी के समूह में नहीं जाता, नौकर को कभी भी सिंहासन पर बैठकर सम्मान नहीं मिलता, दुष्ट महिला को सज्जनों के समूह में खुशी नहीं होती, जिसकी प्रकृति उसे छोड़ना बहुत मुश्किल है, सावधान रहें।

परोपकाराय फलन्ति वृक्षाः परोपकाराय वहन्ति नद्यः। परोपकाराय दुहन्ति गावः परोपकारार्थ मिदं शरीरम्।। भावार्थ: वृक्ष दान के लिए फल देते हैं, नदियाँ दान के लिए बहती हैं और गायें दान के लिए दूध देती हैं, अर्थात यह शरीर भी दान के लिए है।

नैर्मल्यं वपुषः तवास्ति वसतिः पद्माकरे जायते मन्दं याहि मनोरमां वद गिरं मौनं च सम्पादय धन्यस्त्वं बक राजहंसपदवीं प्राप्नोषि किं तैर्गुणैः नीरक्षीरविभागकर्मनिपुणा शक्तिः कथं लभ्यते।। भावार्थ: शास्त्र कहते हैं कि गुण आपके स्वभाव और जीवन में केवल अनुकूलता से नहीं आते हैं, यह आपके आत्मविश्वास, दृढ़ संकल्प और विश्वास पर निर्भर है। जैसे बगुले का शरीर पवित्र होता है, कमल की तरह धीरे-धीरे चलता है, मधुर वाणी बोलता है और चुप रहता है, लेकिन क्या इसमें हंस की तरह दूध से पानी निकालने की कला है?

छायामन्यस्य कुर्वन्ति तिष्ठन्ति स्वयमातपे। फलान्यपि परार्थाय वृक्षाः सत्पुषा ईव।। भावार्थ: दूसरों को छाया देता है, वह स्वयं धूप में खड़ा होता है, फल भी दूसरों के लिए होते हैं, वास्तव में वृक्ष संत के समान होते हैं।

इन्टुं निन्दति तस्करो गृहपतिं जारो सुशीलं खलः साध्वीमप्यसती कुलीनमकुलो जह्यात् जरन्तं युवा। विद्यावन्तमनक्षरो धनपतिं नीचश्च रूपोज्ज्वलम् वैरूप्येण हतः प्रबुद्धमबुधो कृष्टं निकृष्टो जनः।। भावार्थ: चोर सदा चन्द्रमा की निंदा करता है, व्यभिचारी सज्जन, शील दुष्ट, गंदी स्त्री, धर्मपरायण स्त्री, नीच, कुलीन, युवा वृद्ध, अनपढ़ विद्वान, गरीब, अमीर, कुरूप चेहरा, अज्ञानी बुद्धिमानों की निंदा करता है, और कंगाल मनुष्य भले मनुष्य की निन्दा करता है। है

तडागकृत् वृक्षरोपी इष्टयज्ञश्च यो द्विजः। एते स्वर्गे महीयन्ते ये चान्ये सत्यवादिनः।। भावार्थ: जो द्विज तालाब बनाते हैं, पेड़ लगाते हैं और यज्ञ का अनुष्ठान करते हैं, उन्हें स्वर्ग में महत्व दिया जाता है, इसके अलावा सच बोलने वालों को भी अहमियत मिलती है।

प्रकृत्यैव विभिद्यन्ते गुणा एकस्य वस्तुनः। वृन्ताकः श्लेष्मदः कस्मै कस्मैचित् वातरोग कृत्।। भावार्थ: प्रकृति ने एक ही वस्तु के अलग-अलग गुण दिए हैं। जहां बेगन एक व्यक्ति के लिए कफ का कारक है, वहीं यह दूसरे के लिए वायु रोग का कारण बनता है।

नाप्सु मूत्रं पुरीषं वाष्ठीवनं वा समुत्सृजेत्। अमेध्यमलिप्तमन्यद्वा लोहतं वा विषाणि वा।। भावार्थ: मल, मूत्र, कूड़ाकरकट, रक्त और विष आदि को जल में नहीं फेंकना चाहिए। इससे पानी जहरीला हो जाता है और पर्यावरण पर इसका बुरा असर दिखाई देता है। यह मनुष्यों और अन्य जीवित प्राणियों के स्वास्थ्य को भी नुकसान पहुंचाता है।

चेष्टा वायुः खमाकाशमूष्माग्निः सलिलं द्रवः। पृथिवी चात्र सङ्कातः शरीरं पाञ्चभौतिकम्।। भावार्थ: महर्षि भृगु कहते हैं कि इन वृक्षों के शरीर में गति वायु का रूप है, खोखलापन आकाश का रूप है, ताप अग्नि का रूप है, तरल सलिल का रूप है, ठोसता पृथ्वी का रूप है। इस प्रकार इन वृक्षों का यह शरीर पांच तत्वों- वायु, आकाश, अग्नि, जल और पृथ्वी से बना है।

पुष्पिताः फलवन्तश्च तर्पयन्तीह मानवान्। वृक्षदं पुत्रवत् वृक्षास्तारयन्ति परत्र च।। भावार्थ: फल-फूल वाले वृक्ष मनुष्य को तृप्त करते हैं। वृक्ष देने वाले अर्थात् समाज हित में वृक्ष लगाने वाले परलोक में भी वृक्षों की रक्षा करते हैं।

तस्मात् तडागे सद्वृक्षा रोप्याः श्रेयोऽर्थिना सदा। पुत्रवत् परिपाल्याश्च पुत्रास्ते धर्मतः स्मृताः।। भावार्थ: अत: श्रेयस का अर्थ है कल्याण की इच्छा रखने वाले व्यक्ति को तालाब के पास अच्छे पेड़ लगाने चाहिए और पुत्र की तरह उनकी देखभाल करनी चाहिए। वास्तव में धर्म के अनुसार वृक्षों को ही पुत्र माना गया है।

श्लोक: गोभिर्विप्रैः च वेदैश्च सतीभिः सत्यवादिभिः अलुब्धै र्दानशीलैश्च सप्तभि र्धार्यते मही।। भावार्थ: गाय, ब्राह्मण, वेद, सती स्त्री, सत्यवादी, लोभी और परोपकारी – इन सातों के कारण ही पृथ्वी टिकी हुई है।

दुर्जस्नं सज्जनं कर्तुमुपायो न हि भूतले। अपानं शतधा धौतं न श्रेष्ठमिन्द्रियं भवेत्।। भावार्थ: यह पृथ्वी पर दुष्ट को सज्जन बनाने का तरीका नहीं है। अपान को सौ बार धोने के बाद भी उसे सुपीरियर सेंस नहीं बनाया जा सकता।

लक्ष्मीवन्तो न जानन्ति प्रायेण परवेदनाम्। शेषे धराभारक्लान्ते शेते नारायणः सुखम्।। भावार्थ: लक्ष्मीवन लोग दूसरों का दर्द नहीं समझ सकते। देखो सारी पृथ्वी का भार ढोते हुए कैसे लक्ष्मीपति विष्णु नाग पर सुखपूर्वक सो रहे हैं।

वीरभोग्या वसुन्धरा। भावार्थ: केवल वीर पुरुष ही पृथ्वी का उपभोग कर सकते हैं।

त्यजेदेकं कुलस्यार्थे ग्रामस्यार्थे कुलं त्यजेत्। ग्रामं जनपदस्यार्थे आत्मार्थे पृथिवीं त्यजेत्।। भावार्थ: कुल के हित के लिए त्याग करना चाहिए, गाँव के हित के लिए परिवार का, देश के हित के लिए गाँव का और आत्म-कल्याण के लिए पृथ्वी का त्याग करना चाहिए।

तुरगशतसहस्रं गोगजानां च लक्षं कनकरजत पात्रं मेदिनी सागरान्ता। सुरयुवति समानं कोटिकन्याप्रदानं न हि भवति समानं चान्नदानात्प्रधानम्।। भावार्थ: हजारों घोड़े, लाख गाय-हाथी, सोने-चांदी के पात्र, समुद्र तक धरती, अप्सरा जैसी करोड़ों कन्याओं का दान अन्नदान से बढ़कर कुछ नहीं है।

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essay about nature in sanskrit

World Environment Day in Sanskrit

June 4, 2023

संरक्षेद्दूषितो न स्याल्लोकः मानवजीवनम्।

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पर्यावरण संस्कृत निबंध- Essay on Environment in Sanskrit

In this article, we are providing information on Environment in Sanskrit ( paryavaran nibandh in Sanskrit ) – पर्यावरण संस्कृत निबंध, Essay on Environment in Sanskrit Language for students and kids. Checkout article on Sanskrit Essay list

पर्यावरण संस्कृत निबंध- Essay on Environment in Sanskrit

Paryavaran Par Nibandh Sanskrit Mein

पर्यावरणम् अथवा नैसर्गिकपर्यावरणम् इति शब्दः बहूनि वस्तूनि सूचयति । पृथिव्यां निसर्गतः सतां सजीवानां तथा च निर्जीवानां निर्देशः पर्यावरणशब्देन क्रियते । सर्वे पशवः सर्वे खगाः वृक्षाः लताः पर्णपुष्पफलानि इति एतेषां पर्यावरणे अन्तर्भावः अस्ति । तथैव मृत्तिका, प्रस्तराः पृथ्वीं परितः वातावरणम् इति एते अपि सर्वे घटकाः पर्यावरणशब्दे सम्मीलिताः । यद्यपि मनुजः अपि पर्यावरणस्य घटकः तथापि सः एव एतत् विस्मृतवान् अस्ति । “पर्यावरणस्य हानिः मा भवतु ” इति तु सर्वे विचारवन्तः शास्त्रज्ञाः वा वारंवारं ब्रुवन्ति । किन्तु पर्यावरणस्य हानिः नाम किम् इति एव अद्यापि सामान्यमनुजेन न अवगतम्। प्रदूषणम् अस्वच्छता च इति द्वौ प्रमुखौ शत्रू पर्यावरणस्य । मनुजः एव एतौ रिपू निर्मितवान् । उपरि निर्दिष्टाः अन्ये सर्वे घटकाः नैसर्गिकनियमैः एव जीवनम् आचरन्ति। एकः एव मनुजः स्वबुद्ध्या गर्विष्ठः अनैसर्गिकतां निकटीकरोति । यदि पर्यावरणस्य ह्रासः भवेत् तर्हि तस्य सर्वे घटकाः पीडिताः भवेयुः । मनुजः अपि तेषु घटकेषु एकः अतः निश्चितं नङ्क्ष्यति ।

# Environment in Sanskrit # A Short Essay on Environment in Sanskrit

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ध्यान दें – प्रिय दर्शकों Essay on Environment in Sanskrit आपको अच्छा लगा तो जरूर शेयर करे ।

पर्यावरण पर संस्कृत मे निबंध: | Essay on Save Environment In Sanskrit

इस धरती पर रहने वाली सभी जीवित चीजें पर्यावरण के अंतर्गत आती हैं। चाहे वे जमीन पर रहें या पानी पर वे पर्यावरण का हिस्सा हैं। पर्यावरण में हवा, पानी, सूरज की रोशनी, पौधे, जानवर आदि शामिल हैं।

पर्यावरण में वह सभी प्राकृतिक संसाधन शामिल है जो कई तरीकों से हमारी मदद करते हैं तथा चारों ओर से हमें घेरे हुएं हैं। यह हमें बढ़ने तथा विकसित होने का बेहतर माध्यम देता है, यह हमें वह सब कुछ प्रदान करता है जो इस ग्रह पर जीवन यापन करने हेतु आवश्यक है।

पर्यावरण पर संस्कृत मे निबंध- 10 Lines

वयं वायुजलमृदाभिः आवृत्ते वातावरणे निवसामः ।
एतदेव – जीवनोपयोगिवस्तुनि प्राप्नुमः ।
जलं वायुः च जीवने महत्वपूर्णो स्तः।
साम्प्रतं शुद्ध – पेय जलस्य समस्या वर्तते।
अधुना वायुरपि शुद्धं नास्तिएवमेव प्रदूषित पर्यावरणेन विविधाः रोगाः जायन्तेपर्यावरणस्य रक्षायाः अति आवश्यकता वर्तते।
प्रदूषणस्य अनेकानि कारणानि सन्ति ।
औद्यौगिकापशिष्ट – पदार्थ – उच्च – ध्वनि – यानधूम्रादयः प्रमुखानि कारणानि सन्ति।
पर्यावरणरक्षायै वृक्षाः रोपणीयाः ।
वयं नदीषु तडागेषु च दूषितं जलं न पतेम्।
तैल रहित वाहनानां प्रयोगः करणीयः । जनाः तरुणां रोपणम् अभिरक्षणं च कुर्युः वातावरणं पर्यावरण कथ्यते पर्यावरणेनैव वयं

ये भी पढे:

संस्कृत में कालिदास का निबंध

मम दिनचर्या संस्कृत निबंध

संस्‍कृत भाषाया: महत्‍वम् निबंध:

गणतंत्र दिवस पर संस्कृत में निबंध

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ENVIRONMENTAL AWARENESS IN CLASSICAL SANSKRIT LITERATURE

2012, isara solutions

In olden days, man, as part and parcel of nature, used to live harmoniously with it. He even treated the forces of nature as divine beings—Agni Deva, Varuna Deva, Vayu Deva and glorified their existence and prayed for their intervention in nature’s fury. This paper focuses on the environmental awareness in the Sanskrit literature.

Related Papers

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

The great contemplation of the rise of Indian religion, philosophy, spirituality, culture and civilization is the result of the great merit of Indian seekers, sages, ascetics and achayas. The Indian sage, illuminated by knowledge, was a unique scientist, researcher, unique thinker of humanity, a visionary and a leading philosopher. He was well aware of the truth by Swadhyaya that nothing is meaningless under the constitution established by nature. Surya, air, water, land, flora, fauna, nature, all are always cooperative in the interest of each other. He is auspicious for man. Therefore, it is the moral religion of man to use these environmental substances properly and always continue to preserve them.

Interal Res journa Managt Sci Tech

The present article Environmental Vision In Sanskrit Religious Scriptures reflects upon the pivotal role of our epics-Ramayana and Mahabharata, Puranas and Samaritigranthas in ecology and conservation. Our ancient Hindu scriptures have been written in Sanskrit language as it is considered the oldest language in human history. These scriptures of the Hindu religion have given a detailed description of trees, plants, wildlife and brought out the importance of these Nature objects for people. We get the message for preservation of environment and ecological balance from these religious literatures. These scriptures are our most precious heritage, literature which is truly Eco-centric. 1 They are the best source of human ideals which are said to be revealed to Indian seers. Their ideas and thoughts are also perceived to be wholesome and respectable since time immemorial. The subject matter contained herein is theoretical and practical which reflects the thinking of Indian seers. The oldest epics in the world history of literature are the Ramayana and Mahabharata. They have been a perennial source of spiritual, cultural and artistic inspiration, not only to the people of India, but also to the people all over the world. They have many vivid descriptions of Nature, forests, trees, plants and gardens. The people of Ramayana and Mahabharata worshipped rivers, lakes, forests, trees, air and Nature and developed their relationship with animals as their friends. Let us examine, what environment they created and passed over to us! Our seers loved to live in forests and they used to rear trees as their own. They considered that the trees absorb carbon dioxide and emit oxygen (pranavayu) and they are the lifeline (Pran) of all living beings. The destruction of banyan, peepal, mango and tulsi was prohibited. Man lived in harmony with Nature (Prakriti) and there was hardly any action to disturb the environment. Both the Epics encouraged afforestation and condemned the deforestation. The Mahabharata states that " even if there is only one tree full of flowers and fruits in the village, that place becomes worthy of worship and respect. " 2 Ramayana and Mahabharata are blessed with the rich greenery of nature where rivers are full of neat and clean water and the breeze is pure, nourishing, and healthy. Planting trees and digging ponds 1 Falguni P. Desai: Ecological Ethics in Vedic Metaphysics An Effectual Method To Indoctrinate

Dr. Kaushik Acharya

The Vedas are the first texts in the literature of human race. They deal with knowledge, both physical and spiritual. The Vedic views revolve around the concept of Nature and life. We can easily figure it out how nature was related to life and livelihood of Vedic people through their literature which is referred to as The Vedas. This paper attempts to explore the awareness of ancient Indian people about Environment.

Asian Journal of Multidimensional Research

Antarleen Sinha , Archana Verma

Mankind has always tried to comprehend different natural occurrences and environmental features which surround them as free gifts of nature be it mountains, rivers, rainfall and vegetation. Since ancient times, these gifts of nature allowed human beings to expand their activities and develop from being primitive hunter-gatherers to pastoral nomads and then to settled agriculturists. However, the seers and thinkers of the ancient times understood the fact that while gifts of nature could be found and used aplenty, attempts to exploit nature beyond the boundaries of stability would lead to the nature's wrath. Thus, the idea of utilizing nature with due consideration to maintaining its integrity, along with other inhabitants of the environment, was the ancient Indian perception of sustainable living which was rooted in environmental consciousness.

Piyush S Desai

jayita pramanik

This article focuses on the environment and its related things mentioned in the Simad Bhagavad Gita, the holy book to the Hindu community. The Aryan civilization, which molded to the Hindu or ancient Indian civilization was a riverine one, thus nature was in the mind of the inhabitants. This was reflected in most of the scriptures and the Gita is not the exception. In the current article we have mainly pointed to the slokas (the verses) describing the nature and relation of living with it, human nature and behavior connected to the nature, the evaluation of the nature and its livingetc.

Sahityasetu - A Peer Reviewed Literary e-journal

Dr. Rakesh Patel

Literature has undergone a massive change with the course of the time. It holds the mirror up to nature. Environmental crisis is one of the major hazards encountered by the world today. Literature now addresses the current ecological crisis prevalent all around the globe. Ecocriticism has emerged as a new branch of study which observes the interrelationship between literature and environment. This paper aims at exploring the concern for nature and environment in Indian English poetry. The paper makes an ecocritical analysis of Indian poetry written in English and the diverse attitudes of the poets towards the environment and the need for conserving nature.

Our Heritage

Professor Anup Kumar Dey

Modern world is experiencing various environmental disasters due to the overexploitation of natural resources to satisfy the zeal of maximizing profit and to keep in tune with the technological advancement. This also resulted in the growing consciousness among a good section of people for the conservation of natural resources and keeping the ecological balance. In this context it is to be mentioned that people in ancient India had a fairly good understanding of ecology and this aspect is reflected in various Indian scriptures and literatures. The Vedas put great importance to the protection of environment and maintenance of purity. Instead of exploiting nature it teaches human beings to live in harmony with nature and also to recognize the divine soul prevailing in all living and non-living elements. Vedic culture and scriptures have profoundly stated about ecosystems and the requirement for maintaining the balance.

Jigyasa Journal BHU

Aparna Varma

Hinduism is not only one of the major religions of the world but also a way of life. However, like any other religion, Hinduism is complex if not obfuscating. The disparate shade of Hinduism as a way of life has been manifested in the environmental horizons too. Nature has been an important part of the Hindu belief system. Belief in the Vedas, karma, the ritual of offering flowers and fruits to a deity, vegetarianism, yoga, offerings to snake, etc is some of the practices which one identifies with Hinduism. This paper attempts to explore and not verify the practices followed in Hinduism and their association with the environment-in the form of deities who are prayed, practices associated with Hindu festivals, belief systems and faith. While there are a plethora of ancient scripts dealing with Hinduism, the purpose of this paper will be restricted to the exploration of those aspects of Hinduism that are enmeshed with the environment. Finally, this paper also explores how some of the ancient practices have created a place for themselves in contemporary times and how they are affecting the lives of the people at large.

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Environment Essay in Sanskrit language

Essay on Environment in Sanskrit language

नमस्कार दोस्तों आज का आर्टिकल में हम पर्यावरण के बारे में (Environment Essay in Sanskrit language) 10 वाक्य संस्कृत भाषा में आपके साथ शेयर करने जा रहे हैं परीक्षा में अक्सर पर्यावरण पर 10 वाक्य पूछे जाते हैं अतः यह आर्टिकल आपके लिए बहुत हेल्पफुल होगा

पर्यावरण से ही हम है, हर किसी के जीवन के लिए पर्यावरण का बहुत महत्व है, क्योंकि पृथ्वी पर जीवन, पर्यावरण से ही संभव है। समस्त मनुष्य, जीव-जंतु, प्राकृतिक वनस्पतियां, पेड़-पौड़े, मौसम, जलवायु सब पर्यावरण के अंतर्गत ही निहित हैं। पर्यावरण न सिर्फ जलवायु में संतुलन बनाए रखने का काम करता है

पर्यावरण और मनुष्य एक दूसरे के बिना अधूरे हैं, अर्थात् मनुष्य पर्यावरण पर पूरी तरह से निर्भय है, पर्यावरण के बिना मनुष्य अपने जीवन की कल्पना भी नहीं कर सकता है।

10 line on Environment in Sanskrit Language

इन्हें भी पढ़ें :-.

Importance of Trees Essay in Sanskrit

Essay on Pustakam in Sanskrit | For Class 8

Essay on Raksha Bandhan in Sanskrit for Class 10th |रक्षाबंधन का निबंध संस्कृत में

दोस्तों इस आर्टिकल में आज हम (Essay on Raksha Bandhan in Sanskrit) रक्षाबंधन का निबंध संस्कृत भाषा में आपके साथ शेयर करने जा रहे हैं जो की परीक्षा के लिए बहुत ही महत्वपूर्ण हैं परीक्षा में त्यौहार से संबंधित टॉपिक पर निबंध लिखने का अवश्य ही पूछा जाता है हमारे इस आर्टिकल में हमने रक्षाबंधन के 10 वाक्य के साथ-साथ Long Eassy को भी शामिल किया है जिससे कि आपको इसी याद करने में आसानी होगी और आप परीक्षा में अच्छे अंक अर्जित कर सकते हैं.

रक्षाबंधन भारतीय संस्कृति में महत्वपूर्ण त्योहार में से एक है जो भाई बहन के प्यार और संबंध कोदर्शाता है यह पर्वश्रावण मास के पूर्णिमा तिथि को मनाया जाता है इस दिन बहन अपने भाई की कलाई पर राखी बनती है जिसका मतलब होता है कि भाई अपनी बहन की रक्षा करेगा इसके साथ ही भाई अपनी बहन को उपहार देता है रक्षाबंधन एक परिवार में खुशियों और एकता की भावना को बढ़ावा देता है साथ ही भाई बहन के बीच विशेष संबंध को मजबूती प्रदान करता है.

इस दिन प्रात स्नान आदि करके बहने पूजा की थालियां सजाती हैं थाली में राखी के साथ रोली, हल्दी, चावल दीपक, मिठाई और फूल रखती हैं इसके बाद टिका करवाने के लिए भाई को उपयुक्त आसान देती है रोली या हल्दी से भाई का टिका करके चावल को टिके पर लगाया जाता है और सर पर फूलों को छिड़का जाता है उसकी आरती उतारी जाती है, और दाहिनी कलाई पर राखी बांधी जाती है भाई बहन को उपहार या धन देता है इस प्रकार रक्षाबंधन के अनुष्ठान को पूरा करने के बाद ही भोजन किया जाता है.

10 Sentence on Raksha Bandhan in Sanskrit

1. भारतदेश : उत्सवप्रिय : अस्ति , अत्र प्रत्येक मासे दिने व कोऽपि न कोऽपि उत्सव : भवति एव ।

2. येषु अति प्रसिद्धं उत्सव : अस्ति रक्षाबंधन : ।

3. अयम् भ्रातृ भगिन्योः बन्दनस्य पर्वः ।

4. रक्षाबंधन दिवसे भगिनी निज भ्रातु : राखी मणिबन्धनं करोति ।

5. तथांच भ्राता तस्या : रक्षणाय वचनं ददाति ।

6. रक्षाबन्दनस्य प्रतीक रूपमेव राखी ।

7. उत्सव : अयं भ्राता भगिनी च स्नेहस्य प्रतीक : अस्ति ।

8. रक्षाबंधनस्य अयं पवित्रं उत्सवं आर्थिक दृष्ट्या न पश्येयु : ।

9. अस्माकं आपणात् मूल्यवान् राखी न क्रीत्वा साधारणं सूत्रम् एव प्रयोगं कुर्यात्

10. सर्वे संतोषेण उत्साहेन आचरन्ति ।

Long Essay on Raksha Bandhan in Sanskrit

रक्षाबन्धनं श्रावणमासस्य शुक्लपूर्णिमायाम् आचर्यते । भ्रातृभगिन्योः पवित्रसम्बन्धस्य सम्मानाय एतत् पर्व भारतीयाः आचरन्ति । निर्बलतन्तुना बद्धः भ्रातृभगिन्योः सबलसम्बन्धः भारतीयसंस्कृतेः गहनतायाः प्रतीकः । मानवसभ्यतायां विकसिताः सर्वाः संस्कृतयः प्रार्थनायाः माहात्म्यं भूरिशः उपस्थापयन्ति । आदिभारतीयसंस्कृतेः विचारानुगुणं भ्रातुः रक्षायै भगिन्या ईश्वराय कृता प्रार्थना एव रक्षाबन्धनम् । भगिनी ईश्वराय प्रार्थनां करोति यत् , “ हे ईश्वर ! मम भ्रातुः रक्षणं करोतु ” इति । एतां प्रार्थना कुर्वती भगिनी भ्रातुः हस्ते रक्षासूत्रबन्धनं करोति । भगिन्याः हृदि स्वं प्रति निःस्वार्थ प्रेम दृष्ट्वा भ्राता भगिन्यै वचनं ददाति यत् , “ अहं तव रक्षां करिष्ये ” इति । ततः उभौ परस्परं मधुरं भोजयतः । भगिन्या ईश्वराय स्वरक्षणस्य या प्रार्थना कृता , तस्याः प्रार्थनायाः कृते भगिनीं प्रति कृतज्ञता प्रकटयितुं भ्राता भगिन्यै उपहारम् अपि यच्छति । भ्रातृभगिन्योः सम्बन्धस्य एतत् आदानप्रदानम् अमूल्यं वर्तते ।

List of all Animal Name in Sanskrit and Hindi|जानवरों के नाम संस्कृत भाषा में

दोस्तों हमारी पृथ्वी पर पाए जाने वाले जीवो में जानवर, पक्षी और जलीय जीव आते है। अब बात आती है कि जानवर कितने प्रकार के होते है। मुख्यतः जानवरो को जंगली और पालतू जानवरों में विभाजित किया जा सकता है।

जंगली जानवर वो होते है जो जंगलो में आबादी से दूर होते है। इन जानवरों में भी दो मुख्य प्रकार होते है। एक होते है मांसाहारी और दूसरे होते है शाकाहारी। मांसाहारी जानवरो में शेर , चीता, बाघ , भेड़िया जैसे प्राणी आते है। शाकाहारी जानवरो में हिरण, खरगोश , बंदर आदि आते है।

पृथ्वी पर हर तरह की परिस्थिति के अनुकूल जंतु पाये जाते है। करोडों सालों पहले धरती पर डायनासोर भी पाये जाते थे। यह एक भीमकाय सरीसृप प्रजाति का जीव था। इनके अलावा ड्रैगन भी धरती पर पाये जाते थे।

इस आर्टिकल में हम धरती पर पाए जाने वाले कुछ जानवरों के नाम आपके साथ संस्कृत और हिंदी भाषा (All Animal Name in Sanskrit and Hindi) में शेयर करने जा रहे हैं, जो अक्सर परीक्षा में पूछ लिए जाते हैं इस दृष्टि से इनका अभ्यास एक बार अवश्य करें

जलीय जीवों के नाम संस्कृत में (Water Animals Names In Sanskrit)

जंगली जानवरों के नाम संस्कृत में (wild animals names in sanskrit), पालतू जानवरों के नाम संस्कृत में (pet animals names in sanskrit).

दोस्तों उपरोक्त आर्टिकल में शेयर की गई जानकारी (All Animal Name in Sanskrit and Hindi) आपको कैसी लगी हमें कमेंट करके जरूर बताइएगा और ऐसे ही अन्य महत्वपूर्ण टॉपिक से संबंधित जानकारी के लिए हमारी वेबसाइट पर विजिट करते रहिएगा, धन्यवाद!

Fruits Name in Sanskrit Language|| फलों के नाम संस्कृत में

संस्कृत में मम परिचय | Mam Parichay Sanskrit Mein Class 10th

CTET January 2024: केंद्रीय शिक्षक पात्रता परीक्षा के लिए गणित शिक्षण शास्त्र से जुड़े महत्वपूर्ण प्रश्न

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An Essay On The Nature, Age, And Origin Of The Sanskrit Writing And Language (1838) Paperback – January 26, 2009

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Sanskrit Essay on India

This post is an essay on India in Sanskrit.

भारत देश पर संस्कृत में निबंध।

भारतदेशः इति विषये संस्कृतभाषायां निबन्धः।.

Translation is given in Hindi and English for better understanding.

This short essay can be referenced by school students and interested Sanskrit learners.

Table of Contents

Video of Essay on India in Sanskrit

Interested learners can watch the below given video for correct Sanskrit pronunciation after they are done reading and understanding the short essay on India / Bharat.

भारतम् अस्माकं देशः अस्ति। भारतं जगति सप्तमः विशालतमः देशः अस्ति। प्राचीनकाले भरतः इति नामकः पराक्रमी राजा आसीत्। तस्य नाम्नः एव अस्माकं देशस्य नाम जातम् – भारतम्। भारतस्य उत्तरदिशि हिमालयपर्वताः विराजन्ते दक्षिणदिशि च हिन्दमहासागरः वर्तते। पूर्वदिशि बङ्गोपसागरः पश्चिमदिशि च अरबसागरः स्तः। भारतराष्ट्रे पवित्राः नद्यः वहन्ति, यथा – गङ्गा, यमुना, आदिनद्यः। भारतस्य प्राकृतिकसौन्दर्यम् अपि बहुदर्शनीयम् अस्ति। भाषासु, वेशेषु, धर्मेषु च भारते ‘विविधतायाम् एकता’ सदैव वर्तते। भारतदेशः एकं महत् राष्ट्रम् अस्ति।

bhāratadeśaḥ iti viṣaye saṃskṛtabhāṣāyāṃ nibandhaḥ।

bhāratam asmākaṃ deśaḥ asti। bhārataṃ jagati saptamaḥ viśālatamaḥ deśaḥ asti। prācīnakāle bharataḥ iti nāmakaḥ parākramī rājā āsīt। tasya nāmnaḥ eva asmākaṃ deśasya nāma jātam – bhāratam। bhāratasya uttaradiśi himālayaparvatāḥ virājante dakṣiṇadiśi ca hindamahāsāgaraḥ vartate। pūrvadiśi baṅgopasāgaraḥ paścimadiśi ca arabasāgaraḥ staḥ। bhāratarāṣṭre pavitrāḥ nadyaḥ vahanti, yathā – gaṅgā, yamunā, ādinadyaḥ। bhāratasya prākṛtikasaundaryam api bahudarśanīyam asti। bhāṣāsu, veśeṣu, dharmeṣu ca bhārate ‘vividhatāyām ekatā’ sadaiva vartate। bhāratadeśaḥ ekaṃ mahat rāṣṭram asti।

Essay On India

Bharat is our country. Bharat is the seventh largest country in the world. Bharata was a valorous king during the ancient times. It is from his name that our country’s name was derived – Bharat. In the north of Bharat, the Himalayan mountains are situated, and the Indian Ocean lies to the south of Bharat. In the east, there is the Bay of Bengal and the Arabian Sea lies to the west. Many holy rivers flow through Bharat, like the Ganga, the Yamuna, etc. The nature of Bharat is also quite beautiful. The principle of ‘Unity in DIversity’ can be experienced in the linguistic, clothing and religious cultures of India. Bharat is a great country.

भारत हमारा देश है। भारत जग में सातवाँ सबसे विशाल देश है। प्राचीन काल में भरत नाम के एक पराक्रमी राजा थे। हमारे देश का नाम उन्हीं के नाम पर रखा गया है। भारत के उत्तर में हिमालय पर्वत विराजमान हैं और हिंद महासागर भारत के दक्षिण में स्थित है। पूर्व में बंगाल की खाड़ी और पश्चिम में अरब सागर हैं। भारत में अनेक पवित्र नदियाँ बहती हैं, जैसे – गंगा, यमुना आदि। भारत में प्राकृतिक सौंदर्य भी बहुत दर्शनीय है। हमारे देश में भाषाओं, वेशों और धर्मों में ‘विविधता में एकता’ सदैव रहती है। भारत एक महान राष्ट्र है।

Importance of Machines

Importance of Art

Republic Day of India

Examination

Pandita Ramabai

Teachers Day

The Experience of Covid

Essay on Ganeshotsav

Importance of Indian Festivals

Importance of Discipline

Importance of Sports

Nutritious Diet

Books My Friend

Mahatma Gandhi

Lokmanya Tilak

Importance of Knowledge

Makar Sankranti

Indian National Flag

Importance of Cleanliness

Importance of Exercise

Importance of Water

Summer Season

Rainy Season

My favourite Teacher

Indian Independence Day

Raksha Bandhan

International Yoga Day

World Environment Day

River Ganga

Abdul Kalam

Goddess Saraswati

Sanskrit Language

Festival of Holi

Shri Krishna

Shri Ganesha

Other interesting sections.

Learn about Sanskrit Axioms which is a statement or proposition which is regarded as being established, accepted, or self-evidently true.

Learn about Sanskrit Proverbs which are a short, well-known saying, stating a general truth or piece of advice.

Learn about Shloka or shlokas in Sanskrit which consists of four padas of 8 syllables each, or of two half-verses of 16 syllables each.

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Save Environment Essay in Sanskrit - पर्यावरण संरक्षण संस्कृत निबंध : निरंतरं वर्धमानं प्रदूषणं सर्वेषां चिन्तायाः विषयः जातः। यत: प्रदूषणेन अधिका: मनुजाः रुग्णाः भवन्ति। अनेन प्रकृत्याः हानिर्भवति, जीवनामपि हानिर्भवति। अतः प्रदूषणसमस्यायाः समाधाने वैज्ञानिकाः प्रयतन्ते किन्तु पर्यावरणस्य रक्षा अस्माभिः सर्वे: करणीया। यावत् देशस्य नागरिक: पर्यावरणस्य रक्षणे दृढनिश्चयः न भवति, तावत् अस्याः समस्या समाधानं न भवति। स्थाने-स्थाने विविधा: वृक्षा रोपणीयाः। तेषां च संवर्धनमपि अवश्यं कर्त्तव्यम्। विद्यालयेषु ‘वृक्षारोपण' दिवसस्य आयोजनं भवेत्। वृक्षारोपणं सर्वेषां नैतिकं कर्तव्यमस्ति। जलप्रदूषणनिवारणार्थं जलशुद्धिः करणीया। अवकरः मार्ग न क्षेपणीयः। सर्वत्र पर्यावरणविषये गोष्ठीनाम् आयोजनं भवेत्।

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Published: 06 May 2024

APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease

Juan Fortea ORCID: orcid.org/0000-0002-1340-638X 1 , 2 , 3 na1 ,
Jordi Pegueroles ORCID: orcid.org/0000-0002-3554-2446 1 , 2 ,
Daniel Alcolea ORCID: orcid.org/0000-0002-3819-3245 1 , 2 ,
Olivia Belbin ORCID: orcid.org/0000-0002-6109-6371 1 , 2 ,
Oriol Dols-Icardo ORCID: orcid.org/0000-0003-2656-8748 1 , 2 ,
Lídia Vaqué-Alcázar 1 , 4 ,
Laura Videla ORCID: orcid.org/0000-0002-9748-8465 1 , 2 , 3 ,
Juan Domingo Gispert 5 , 6 , 7 , 8 , 9 ,
Marc Suárez-Calvet ORCID: orcid.org/0000-0002-2993-569X 5 , 6 , 7 , 8 , 9 ,
Sterling C. Johnson ORCID: orcid.org/0000-0002-8501-545X 10 ,
Reisa Sperling ORCID: orcid.org/0000-0003-1535-6133 11 ,
Alexandre Bejanin ORCID: orcid.org/0000-0002-9958-0951 1 , 2 ,
Alberto Lleó ORCID: orcid.org/0000-0002-2568-5478 1 , 2 &
Víctor Montal ORCID: orcid.org/0000-0002-5714-9282 1 , 2 , 12 na1

Nature Medicine ( 2024 ) Cite this article

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Alzheimer's disease
Predictive markers

This study aimed to evaluate the impact of APOE4 homozygosity on Alzheimer’s disease (AD) by examining its clinical, pathological and biomarker changes to see whether APOE4 homozygotes constitute a distinct, genetically determined form of AD. Data from the National Alzheimer’s Coordinating Center and five large cohorts with AD biomarkers were analyzed. The analysis included 3,297 individuals for the pathological study and 10,039 for the clinical study. Findings revealed that almost all APOE4 homozygotes exhibited AD pathology and had significantly higher levels of AD biomarkers from age 55 compared to APOE3 homozygotes. By age 65, nearly all had abnormal amyloid levels in cerebrospinal fluid, and 75% had positive amyloid scans, with the prevalence of these markers increasing with age, indicating near-full penetrance of AD biology in APOE4 homozygotes. The age of symptom onset was earlier in APOE4 homozygotes at 65.1, with a narrower 95% prediction interval than APOE3 homozygotes. The predictability of symptom onset and the sequence of biomarker changes in APOE4 homozygotes mirrored those in autosomal dominant AD and Down syndrome. However, in the dementia stage, there were no differences in amyloid or tau positron emission tomography across haplotypes, despite earlier clinical and biomarker changes. The study concludes that APOE4 homozygotes represent a genetic form of AD, suggesting the need for individualized prevention strategies, clinical trials and treatments.

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APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia

The Amyloid-β Pathway in Alzheimer’s Disease

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants

Data availability.

Access to tabular data from ADNI ( https://adni.loni.usc.edu/ ), OASIS ( https://oasis-brains.org/ ), A4 ( https://ida.loni.usc.edu/collaboration/access/appLicense.jsp ) and NACC ( https://naccdata.org/ ) can be requested online, as publicly available databases. All requests will be reviewed by each studyʼs scientific board. Concrete inquiries to access the WRAP ( https://wrap.wisc.edu/data-requests-2/ ) and ALFA + ( https://www.barcelonabeta.org/en/alfa-study/about-the-alfa-study ) cohort data can be directed to each study team for concept approval and feasibility consultation. Requests will be reviewed to verify whether the request is subject to any intellectual property.

Code availability

All statistical analyses and raw figures were generated using R (v.4.2.2). We used the open-sourced R packages of ggplot2 (v.3.4.3), dplyr (v.1.1.3), ggstream (v.0.1.0), ggpubr (v.0.6), ggstatsplot (v.0.12), Rmisc (v.1.5.1), survival (v.3.5), survminer (v.0.4.9), gtsummary (v.1.7), epitools (v.0.5) and statsExpression (v.1.5.1). Rscripts to replicate our findings can be found at https://gitlab.com/vmontalb/apoe4-asdad (ref. 32 ). For neuroimaging analyses, we used Free Surfer (v.6.0) and ANTs (v.2.4.0).

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Acknowledgements

We acknowledge the contributions of several consortia that provided data for this study. We extend our appreciation to the NACC, the Alzheimer’s Disease Neuroimaging Initiative, The A4 Study, the ALFA Study, the Wisconsin Register for Alzheimer’s Prevention and the OASIS3 Project. Without their dedication to advancing Alzheimer’s disease research and their commitment to data sharing, this study would not have been possible. We also thank all the participants and investigators involved in these consortia for their tireless efforts and invaluable contributions to the field. We also thank the institutions that funded this study, the Fondo de Investigaciones Sanitario, Carlos III Health Institute, the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas and the Generalitat de Catalunya and La Caixa Foundation, as well as the NIH, Horizon 2020 and the Alzheimer’s Association, which was crucial for this research. Funding: National Institute on Aging. This study was supported by the Fondo de Investigaciones Sanitario, Carlos III Health Institute (INT21/00073, PI20/01473 and PI23/01786 to J.F., CP20/00038, PI22/00307 to A.B., PI22/00456 to M.S.-C., PI18/00435 to D.A., PI20/01330 to A.L.) and the Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas Program 1, partly jointly funded by Fondo Europeo de Desarrollo Regional, Unión Europea, Una Manera de Hacer Europa. This work was also supported by the National Institutes of Health grants (R01 AG056850; R21 AG056974, R01 AG061566, R01 AG081394 and R61AG066543 to J.F., S10 OD025245, P30 AG062715, U54 HD090256, UL1 TR002373, P01 AG036694 and P50 AG005134 to R.S.; R01 AG027161, R01 AG021155, R01 AG037639, R01 AG054059; P50 AG033514 and P30 AG062715 to S.J.) and ADNI (U01 AG024904), the Department de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca I Innovació en Salut (SLT006/17/00119 to J.F.; SLT002/16/00408 to A.L.) and the A4 Study (R01 AG063689, U24 AG057437 to R.A.S). It was also supported by Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151 o J.F.) and Horizon 2020–Research and Innovation Framework Programme from the European Union (H2020-SC1-BHC-2018-2020 to J.F.; 948677 and 847648 to M.S.-C.). La Caixa Foundation (LCF/PR/GN17/50300004 to M.S.-C.) and EIT Digital (Grant 2021 to J.D.G.) also supported this work. The Alzheimer Association also participated in the funding of this work (AARG-22-923680 to A.B.) and A4/LEARN Study AA15-338729 to R.A.S.). O.D.-I. receives funding from the Alzheimer’s Association (AARF-22-924456) and the Jerome Lejeune Foundation postdoctoral fellowship.

Author information

These authors contributed equally: Juan Fortea, Víctor Montal.

Authors and Affiliations

Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau - Biomedical Research Institute Sant Pau, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Lídia Vaqué-Alcázar, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas. CIBERNED, Barcelona, Spain

Juan Fortea, Jordi Pegueroles, Daniel Alcolea, Olivia Belbin, Oriol Dols-Icardo, Laura Videla, Alexandre Bejanin, Alberto Lleó & Víctor Montal

Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain

Juan Fortea & Laura Videla

Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain

Lídia Vaqué-Alcázar

Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

Juan Domingo Gispert & Marc Suárez-Calvet

Neurosciences Programme, IMIM - Hospital del Mar Medical Research Institute, Barcelona, Spain

Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina. Instituto de Salud carlos III, Madrid, Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA

Sterling C. Johnson

Brigham and Women’s Hospital Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Reisa Sperling

Barcelona Supercomputing Center, Barcelona, Spain

Víctor Montal

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Contributions

J.F. and V.M. conceptualized the research project and drafted the initial manuscript. V.M., J.P. and J.F. conducted data analysis, interpreted statistical findings and created visual representations of the data. O.B. and O.D.-I. provided valuable insights into the genetics of APOE. L.V., A.B. and L.V.-A. meticulously reviewed and edited the manuscript for clarity, accuracy and coherence. J.D.G., M.S.-C., S.J. and R.S. played pivotal roles in data acquisition and securing funding. A.L. and D.A. contributed to the study design, offering guidance and feedback on statistical analyses, and provided critical review of the paper. All authors carefully reviewed the manuscript, offering pertinent feedback that enhanced the study’s quality, and ultimately approved the final version.

Corresponding authors

Correspondence to Juan Fortea or Víctor Montal .

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Competing interests.

S.C.J. has served at scientific advisory boards for ALZPath, Enigma and Roche Diagnostics. M.S.-C. has given lectures in symposia sponsored by Almirall, Eli Lilly, Novo Nordisk, Roche Diagnostics and Roche Farma, received consultancy fees (paid to the institution) from Roche Diagnostics and served on advisory boards of Roche Diagnostics and Grifols. He was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. In-kind support for research (to the institution) was received from ADx Neurosciences, Alamar Biosciences, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development and Roche Diagnostics. J.D.G. has served as consultant for Roche Diagnostics, receives research funding from Hoffmann–La Roche, Roche Diagnostics and GE Healthcare, has given lectures in symposia sponsored by Biogen, Philips Nederlands, Esteve and Life Molecular Imaging and serves on an advisory board for Prothena Biosciences. R.S. has received personal consulting fees from Abbvie, AC Immune, Acumen, Alector, Bristol Myers Squibb, Janssen, Genentech, Ionis and Vaxxinity outside the submitted work. O.B. reported receiving personal fees from Adx NeuroSciences outside the submitted work. D.A. reported receiving personal fees for advisory board services and/or speaker honoraria from Fujirebio-Europe, Roche, Nutricia, Krka Farmacéutica and Esteve, outside the submitted work. A.L. has served as a consultant or on advisory boards for Almirall, Fujirebio-Europe, Grifols, Eisai, Lilly, Novartis, Roche, Biogen and Nutricia, outside the submitted work. J.F. reported receiving personal fees for service on the advisory boards, adjudication committees or speaker honoraria from AC Immune, Adamed, Alzheon, Biogen, Eisai, Esteve, Fujirebio, Ionis, Laboratorios Carnot, Life Molecular Imaging, Lilly, Lundbeck, Perha, Roche and outside the submitted work. O.B., D.A., A.L. and J.F. report holding a patent for markers of synaptopathy in neurodegenerative disease (licensed to Adx, EPI8382175.0). The remaining authors declare no competing interests.

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Supplementary Methods, Results, Bibliography, Figs. 1–7 and Tables 1–3.

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Fortea, J., Pegueroles, J., Alcolea, D. et al. APOE4 homozygozity represents a distinct genetic form of Alzheimer’s disease. Nat Med (2024). https://doi.org/10.1038/s41591-024-02931-w

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Published: 03 June 2024

Applying large language models for automated essay scoring for non-native Japanese

Wenchao Li 1 &
Haitao Liu 2

Humanities and Social Sciences Communications volume 11 , Article number: 723 ( 2024 ) Cite this article

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Language and linguistics

Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated listening tests, and automated oral proficiency assessments. The application of LLMs for AES in the context of non-native Japanese, however, remains limited. This study explores the potential of LLM-based AES by comparing the efficiency of different models, i.e. two conventional machine training technology-based methods (Jess and JWriter), two LLMs (GPT and BERT), and one Japanese local LLM (Open-Calm large model). To conduct the evaluation, a dataset consisting of 1400 story-writing scripts authored by learners with 12 different first languages was used. Statistical analysis revealed that GPT-4 outperforms Jess and JWriter, BERT, and the Japanese language-specific trained Open-Calm large model in terms of annotation accuracy and predicting learning levels. Furthermore, by comparing 18 different models that utilize various prompts, the study emphasized the significance of prompts in achieving accurate and reliable evaluations using LLMs.

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Conventional machine learning technology in aes.

AES has experienced significant growth with the advancement of machine learning technologies in recent decades. In the earlier stages of AES development, conventional machine learning-based approaches were commonly used. These approaches involved the following procedures: a) feeding the machine with a dataset. In this step, a dataset of essays is provided to the machine learning system. The dataset serves as the basis for training the model and establishing patterns and correlations between linguistic features and human ratings. b) the machine learning model is trained using linguistic features that best represent human ratings and can effectively discriminate learners’ writing proficiency. These features include lexical richness (Lu, 2012 ; Kyle and Crossley, 2015 ; Kyle et al. 2021 ), syntactic complexity (Lu, 2010 ; Liu, 2008 ), text cohesion (Crossley and McNamara, 2016 ), and among others. Conventional machine learning approaches in AES require human intervention, such as manual correction and annotation of essays. This human involvement was necessary to create a labeled dataset for training the model. Several AES systems have been developed using conventional machine learning technologies. These include the Intelligent Essay Assessor (Landauer et al. 2003 ), the e-rater engine by Educational Testing Service (Attali and Burstein, 2006 ; Burstein, 2003 ), MyAccess with the InterlliMetric scoring engine by Vantage Learning (Elliot, 2003 ), and the Bayesian Essay Test Scoring system (Rudner and Liang, 2002 ). These systems have played a significant role in automating the essay scoring process and providing quick and consistent feedback to learners. However, as touched upon earlier, conventional machine learning approaches rely on predetermined linguistic features and often require manual intervention, making them less flexible and potentially limiting their generalizability to different contexts.

In the context of the Japanese language, conventional machine learning-incorporated AES tools include Jess (Ishioka and Kameda, 2006 ) and JWriter (Lee and Hasebe, 2017 ). Jess assesses essays by deducting points from the perfect score, utilizing the Mainichi Daily News newspaper as a database. The evaluation criteria employed by Jess encompass various aspects, such as rhetorical elements (e.g., reading comprehension, vocabulary diversity, percentage of complex words, and percentage of passive sentences), organizational structures (e.g., forward and reverse connection structures), and content analysis (e.g., latent semantic indexing). JWriter employs linear regression analysis to assign weights to various measurement indices, such as average sentence length and total number of characters. These weights are then combined to derive the overall score. A pilot study involving the Jess model was conducted on 1320 essays at different proficiency levels, including primary, intermediate, and advanced. However, the results indicated that the Jess model failed to significantly distinguish between these essay levels. Out of the 16 measures used, four measures, namely median sentence length, median clause length, median number of phrases, and maximum number of phrases, did not show statistically significant differences between the levels. Additionally, two measures exhibited between-level differences but lacked linear progression: the number of attributives declined words and the Kanji/kana ratio. On the other hand, the remaining measures, including maximum sentence length, maximum clause length, number of attributive conjugated words, maximum number of consecutive infinitive forms, maximum number of conjunctive-particle clauses, k characteristic value, percentage of big words, and percentage of passive sentences, demonstrated statistically significant between-level differences and displayed linear progression.

Both Jess and JWriter exhibit notable limitations, including the manual selection of feature parameters and weights, which can introduce biases into the scoring process. The reliance on human annotators to label non-native language essays also introduces potential noise and variability in the scoring. Furthermore, an important concern is the possibility of system manipulation and cheating by learners who are aware of the regression equation utilized by the models (Hirao et al. 2020 ). These limitations emphasize the need for further advancements in AES systems to address these challenges.

Deep learning technology in AES

Deep learning has emerged as one of the approaches for improving the accuracy and effectiveness of AES. Deep learning-based AES methods utilize artificial neural networks that mimic the human brain’s functioning through layered algorithms and computational units. Unlike conventional machine learning, deep learning autonomously learns from the environment and past errors without human intervention. This enables deep learning models to establish nonlinear correlations, resulting in higher accuracy. Recent advancements in deep learning have led to the development of transformers, which are particularly effective in learning text representations. Noteworthy examples include bidirectional encoder representations from transformers (BERT) (Devlin et al. 2019 ) and the generative pretrained transformer (GPT) (OpenAI).

BERT is a linguistic representation model that utilizes a transformer architecture and is trained on two tasks: masked linguistic modeling and next-sentence prediction (Hirao et al. 2020 ; Vaswani et al. 2017 ). In the context of AES, BERT follows specific procedures, as illustrated in Fig. 1 : (a) the tokenized prompts and essays are taken as input; (b) special tokens, such as [CLS] and [SEP], are added to mark the beginning and separation of prompts and essays; (c) the transformer encoder processes the prompt and essay sequences, resulting in hidden layer sequences; (d) the hidden layers corresponding to the [CLS] tokens (T[CLS]) represent distributed representations of the prompts and essays; and (e) a multilayer perceptron uses these distributed representations as input to obtain the final score (Hirao et al. 2020 ).

AES system with BERT (Hirao et al. 2020 ).

The training of BERT using a substantial amount of sentence data through the Masked Language Model (MLM) allows it to capture contextual information within the hidden layers. Consequently, BERT is expected to be capable of identifying artificial essays as invalid and assigning them lower scores (Mizumoto and Eguchi, 2023 ). In the context of AES for nonnative Japanese learners, Hirao et al. ( 2020 ) combined the long short-term memory (LSTM) model proposed by Hochreiter and Schmidhuber ( 1997 ) with BERT to develop a tailored automated Essay Scoring System. The findings of their study revealed that the BERT model outperformed both the conventional machine learning approach utilizing character-type features such as “kanji” and “hiragana”, as well as the standalone LSTM model. Takeuchi et al. ( 2021 ) presented an approach to Japanese AES that eliminates the requirement for pre-scored essays by relying solely on reference texts or a model answer for the essay task. They investigated multiple similarity evaluation methods, including frequency of morphemes, idf values calculated on Wikipedia, LSI, LDA, word-embedding vectors, and document vectors produced by BERT. The experimental findings revealed that the method utilizing the frequency of morphemes with idf values exhibited the strongest correlation with human-annotated scores across different essay tasks. The utilization of BERT in AES encounters several limitations. Firstly, essays often exceed the model’s maximum length limit. Second, only score labels are available for training, which restricts access to additional information.

Mizumoto and Eguchi ( 2023 ) were pioneers in employing the GPT model for AES in non-native English writing. Their study focused on evaluating the accuracy and reliability of AES using the GPT-3 text-davinci-003 model, analyzing a dataset of 12,100 essays from the corpus of nonnative written English (TOEFL11). The findings indicated that AES utilizing the GPT-3 model exhibited a certain degree of accuracy and reliability. They suggest that GPT-3-based AES systems hold the potential to provide support for human ratings. However, applying GPT model to AES presents a unique natural language processing (NLP) task that involves considerations such as nonnative language proficiency, the influence of the learner’s first language on the output in the target language, and identifying linguistic features that best indicate writing quality in a specific language. These linguistic features may differ morphologically or syntactically from those present in the learners’ first language, as observed in (1)–(3).

我-送了-他-一本-书

Wǒ-sòngle-tā-yī běn-shū

1 sg .-give. past- him-one .cl- book

“I gave him a book.”

Agglutinative

彼-に-本-を-あげ-まし-た

Kare-ni-hon-o-age-mashi-ta

3 sg .- dat -hon- acc- give.honorification. past

Inflectional

give, give-s, gave, given, giving

Additionally, the morphological agglutination and subject-object-verb (SOV) order in Japanese, along with its idiomatic expressions, pose additional challenges for applying language models in AES tasks (4).

足-が棒-になり-ました

Ashi-ga bo-ni nar-mashita

leg- nom stick- dat become- past

“My leg became like a stick (I am extremely tired).”

The example sentence provided demonstrates the morpho-syntactic structure of Japanese and the presence of an idiomatic expression. In this sentence, the verb “なる” (naru), meaning “to become”, appears at the end of the sentence. The verb stem “なり” (nari) is attached with morphemes indicating honorification (“ます” - mashu) and tense (“た” - ta), showcasing agglutination. While the sentence can be literally translated as “my leg became like a stick”, it carries an idiomatic interpretation that implies “I am extremely tired”.

To overcome this issue, CyberAgent Inc. ( 2023 ) has developed the Open-Calm series of language models specifically designed for Japanese. Open-Calm consists of pre-trained models available in various sizes, such as Small, Medium, Large, and 7b. Figure 2 depicts the fundamental structure of the Open-Calm model. A key feature of this architecture is the incorporation of the Lora Adapter and GPT-NeoX frameworks, which can enhance its language processing capabilities.

GPT-NeoX Model Architecture (Okgetheng and Takeuchi 2024 ).

In a recent study conducted by Okgetheng and Takeuchi ( 2024 ), they assessed the efficacy of Open-Calm language models in grading Japanese essays. The research utilized a dataset of approximately 300 essays, which were annotated by native Japanese educators. The findings of the study demonstrate the considerable potential of Open-Calm language models in automated Japanese essay scoring. Specifically, among the Open-Calm family, the Open-Calm Large model (referred to as OCLL) exhibited the highest performance. However, it is important to note that, as of the current date, the Open-Calm Large model does not offer public access to its server. Consequently, users are required to independently deploy and operate the environment for OCLL. In order to utilize OCLL, users must have a PC equipped with an NVIDIA GeForce RTX 3060 (8 or 12 GB VRAM).

In summary, while the potential of LLMs in automated scoring of nonnative Japanese essays has been demonstrated in two studies—BERT-driven AES (Hirao et al. 2020 ) and OCLL-based AES (Okgetheng and Takeuchi, 2024 )—the number of research efforts in this area remains limited.

Another significant challenge in applying LLMs to AES lies in prompt engineering and ensuring its reliability and effectiveness (Brown et al. 2020 ; Rae et al. 2021 ; Zhang et al. 2021 ). Various prompting strategies have been proposed, such as the zero-shot chain of thought (CoT) approach (Kojima et al. 2022 ), which involves manually crafting diverse and effective examples. However, manual efforts can lead to mistakes. To address this, Zhang et al. ( 2021 ) introduced an automatic CoT prompting method called Auto-CoT, which demonstrates matching or superior performance compared to the CoT paradigm. Another prompt framework is trees of thoughts, enabling a model to self-evaluate its progress at intermediate stages of problem-solving through deliberate reasoning (Yao et al. 2023 ).

Beyond linguistic studies, there has been a noticeable increase in the number of foreign workers in Japan and Japanese learners worldwide (Ministry of Health, Labor, and Welfare of Japan, 2022 ; Japan Foundation, 2021 ). However, existing assessment methods, such as the Japanese Language Proficiency Test (JLPT), J-CAT, and TTBJ Footnote 1 , primarily focus on reading, listening, vocabulary, and grammar skills, neglecting the evaluation of writing proficiency. As the number of workers and language learners continues to grow, there is a rising demand for an efficient AES system that can reduce costs and time for raters and be utilized for employment, examinations, and self-study purposes.

This study aims to explore the potential of LLM-based AES by comparing the effectiveness of five models: two LLMs (GPT Footnote 2 and BERT), one Japanese local LLM (OCLL), and two conventional machine learning-based methods (linguistic feature-based scoring tools - Jess and JWriter).

The research questions addressed in this study are as follows:

To what extent do the LLM-driven AES and linguistic feature-based AES, when used as automated tools to support human rating, accurately reflect test takers’ actual performance?

What influence does the prompt have on the accuracy and performance of LLM-based AES methods?

The subsequent sections of the manuscript cover the methodology, including the assessment measures for nonnative Japanese writing proficiency, criteria for prompts, and the dataset. The evaluation section focuses on the analysis of annotations and rating scores generated by LLM-driven and linguistic feature-based AES methods.

Methodology

The dataset utilized in this study was obtained from the International Corpus of Japanese as a Second Language (I-JAS) Footnote 3 . This corpus consisted of 1000 participants who represented 12 different first languages. For the study, the participants were given a story-writing task on a personal computer. They were required to write two stories based on the 4-panel illustrations titled “Picnic” and “The key” (see Appendix A). Background information for the participants was provided by the corpus, including their Japanese language proficiency levels assessed through two online tests: J-CAT and SPOT. These tests evaluated their reading, listening, vocabulary, and grammar abilities. The learners’ proficiency levels were categorized into six levels aligned with the Common European Framework of Reference for Languages (CEFR) and the Reference Framework for Japanese Language Education (RFJLE): A1, A2, B1, B2, C1, and C2. According to Lee et al. ( 2015 ), there is a high level of agreement (r = 0.86) between the J-CAT and SPOT assessments, indicating that the proficiency certifications provided by J-CAT are consistent with those of SPOT. However, it is important to note that the scores of J-CAT and SPOT do not have a one-to-one correspondence. In this study, the J-CAT scores were used as a benchmark to differentiate learners of different proficiency levels. A total of 1400 essays were utilized, representing the beginner (aligned with A1), A2, B1, B2, C1, and C2 levels based on the J-CAT scores. Table 1 provides information about the learners’ proficiency levels and their corresponding J-CAT and SPOT scores.

A dataset comprising a total of 1400 essays from the story writing tasks was collected. Among these, 714 essays were utilized to evaluate the reliability of the LLM-based AES method, while the remaining 686 essays were designated as development data to assess the LLM-based AES’s capability to distinguish participants with varying proficiency levels. The GPT 4 API was used in this study. A detailed explanation of the prompt-assessment criteria is provided in Section Prompt . All essays were sent to the model for measurement and scoring.

Measures of writing proficiency for nonnative Japanese

Japanese exhibits a morphologically agglutinative structure where morphemes are attached to the word stem to convey grammatical functions such as tense, aspect, voice, and honorifics, e.g. (5).

食べ-させ-られ-まし-た-か

tabe-sase-rare-mashi-ta-ka

[eat (stem)-causative-passive voice-honorification-tense. past-question marker]

Japanese employs nine case particles to indicate grammatical functions: the nominative case particle が (ga), the accusative case particle を (o), the genitive case particle の (no), the dative case particle に (ni), the locative/instrumental case particle で (de), the ablative case particle から (kara), the directional case particle へ (e), and the comitative case particle と (to). The agglutinative nature of the language, combined with the case particle system, provides an efficient means of distinguishing between active and passive voice, either through morphemes or case particles, e.g. 食べる taberu “eat concusive . ” (active voice); 食べられる taberareru “eat concusive . ” (passive voice). In the active voice, “パンを食べる” (pan o taberu) translates to “to eat bread”. On the other hand, in the passive voice, it becomes “パンが食べられた” (pan ga taberareta), which means “(the) bread was eaten”. Additionally, it is important to note that different conjugations of the same lemma are considered as one type in order to ensure a comprehensive assessment of the language features. For example, e.g., 食べる taberu “eat concusive . ”; 食べている tabeteiru “eat progress .”; 食べた tabeta “eat past . ” as one type.

To incorporate these features, previous research (Suzuki, 1999 ; Watanabe et al. 1988 ; Ishioka, 2001 ; Ishioka and Kameda, 2006 ; Hirao et al. 2020 ) has identified complexity, fluency, and accuracy as crucial factors for evaluating writing quality. These criteria are assessed through various aspects, including lexical richness (lexical density, diversity, and sophistication), syntactic complexity, and cohesion (Kyle et al. 2021 ; Mizumoto and Eguchi, 2023 ; Ure, 1971 ; Halliday, 1985 ; Barkaoui and Hadidi, 2020 ; Zenker and Kyle, 2021 ; Kim et al. 2018 ; Lu, 2017 ; Ortega, 2015 ). Therefore, this study proposes five scoring categories: lexical richness, syntactic complexity, cohesion, content elaboration, and grammatical accuracy. A total of 16 measures were employed to capture these categories. The calculation process and specific details of these measures can be found in Table 2 .

T-unit, first introduced by Hunt ( 1966 ), is a measure used for evaluating speech and composition. It serves as an indicator of syntactic development and represents the shortest units into which a piece of discourse can be divided without leaving any sentence fragments. In the context of Japanese language assessment, Sakoda and Hosoi ( 2020 ) utilized T-unit as the basic unit to assess the accuracy and complexity of Japanese learners’ speaking and storytelling. The calculation of T-units in Japanese follows the following principles:

A single main clause constitutes 1 T-unit, regardless of the presence or absence of dependent clauses, e.g. (6).

ケンとマリはピクニックに行きました (main clause): 1 T-unit.

If a sentence contains a main clause along with subclauses, each subclause is considered part of the same T-unit, e.g. (7).

天気が良かったので (subclause)、ケンとマリはピクニックに行きました (main clause): 1 T-unit.

In the case of coordinate clauses, where multiple clauses are connected, each coordinated clause is counted separately. Thus, a sentence with coordinate clauses may have 2 T-units or more, e.g. (8).

ケンは地図で場所を探して (coordinate clause)、マリはサンドイッチを作りました (coordinate clause): 2 T-units.

Lexical diversity refers to the range of words used within a text (Engber, 1995 ; Kyle et al. 2021 ) and is considered a useful measure of the breadth of vocabulary in L n production (Jarvis, 2013a , 2013b ).

The type/token ratio (TTR) is widely recognized as a straightforward measure for calculating lexical diversity and has been employed in numerous studies. These studies have demonstrated a strong correlation between TTR and other methods of measuring lexical diversity (e.g., Bentz et al. 2016 ; Čech and Miroslav, 2018 ; Çöltekin and Taraka, 2018 ). TTR is computed by considering both the number of unique words (types) and the total number of words (tokens) in a given text. Given that the length of learners’ writing texts can vary, this study employs the moving average type-token ratio (MATTR) to mitigate the influence of text length. MATTR is calculated using a 50-word moving window. Initially, a TTR is determined for words 1–50 in an essay, followed by words 2–51, 3–52, and so on until the end of the essay is reached (Díez-Ortega and Kyle, 2023 ). The final MATTR scores were obtained by averaging the TTR scores for all 50-word windows. The following formula was employed to derive MATTR:

${\rm{MATTR}}({\rm{W}})=\frac{{\sum }_{{\rm{i}}=1}^{{\rm{N}}-{\rm{W}}+1}{{\rm{F}}}_{{\rm{i}}}}{{\rm{W}}({\rm{N}}-{\rm{W}}+1)}$

Here, N refers to the number of tokens in the corpus. W is the randomly selected token size (W < N). ${F}_{i}$ is the number of types in each window. The ${\rm{MATTR}}({\rm{W}})$ is the mean of a series of type-token ratios (TTRs) based on the word form for all windows. It is expected that individuals with higher language proficiency will produce texts with greater lexical diversity, as indicated by higher MATTR scores.

Lexical density was captured by the ratio of the number of lexical words to the total number of words (Lu, 2012 ). Lexical sophistication refers to the utilization of advanced vocabulary, often evaluated through word frequency indices (Crossley et al. 2013 ; Haberman, 2008 ; Kyle and Crossley, 2015 ; Laufer and Nation, 1995 ; Lu, 2012 ; Read, 2000 ). In line of writing, lexical sophistication can be interpreted as vocabulary breadth, which entails the appropriate usage of vocabulary items across various lexicon-grammatical contexts and registers (Garner et al. 2019 ; Kim et al. 2018 ; Kyle et al. 2018 ). In Japanese specifically, words are considered lexically sophisticated if they are not included in the “Japanese Education Vocabulary List Ver 1.0”. Footnote 4 Consequently, lexical sophistication was calculated by determining the number of sophisticated word types relative to the total number of words per essay. Furthermore, it has been suggested that, in Japanese writing, sentences should ideally have a length of no more than 40 to 50 characters, as this promotes readability. Therefore, the median and maximum sentence length can be considered as useful indices for assessment (Ishioka and Kameda, 2006 ).

Syntactic complexity was assessed based on several measures, including the mean length of clauses, verb phrases per T-unit, clauses per T-unit, dependent clauses per T-unit, complex nominals per clause, adverbial clauses per clause, coordinate phrases per clause, and mean dependency distance (MDD). The MDD reflects the distance between the governor and dependent positions in a sentence. A larger dependency distance indicates a higher cognitive load and greater complexity in syntactic processing (Liu, 2008 ; Liu et al. 2017 ). The MDD has been established as an efficient metric for measuring syntactic complexity (Jiang, Quyang, and Liu, 2019 ; Li and Yan, 2021 ). To calculate the MDD, the position numbers of the governor and dependent are subtracted, assuming that words in a sentence are assigned in a linear order, such as W1 … Wi … Wn. In any dependency relationship between words Wa and Wb, Wa is the governor and Wb is the dependent. The MDD of the entire sentence was obtained by taking the absolute value of governor – dependent:

MDD = $\frac{1}{n}{\sum }_{i=1}^{n}|{\rm{D}}{{\rm{D}}}_{i}|$

In this formula, $n$ represents the number of words in the sentence, and ${DD}i$ is the dependency distance of the ${i}^{{th}}$ dependency relationship of a sentence. Building on this, the annotation of sentence ‘Mary-ga-John-ni-keshigomu-o-watashita was [Mary- top -John- dat -eraser- acc -give- past] ’. The sentence’s MDD would be 2. Table 3 provides the CSV file as a prompt for GPT 4.

Cohesion (semantic similarity) and content elaboration aim to capture the ideas presented in test taker’s essays. Cohesion was assessed using three measures: Synonym overlap/paragraph (topic), Synonym overlap/paragraph (keywords), and word2vec cosine similarity. Content elaboration and development were measured as the number of metadiscourse markers (type)/number of words. To capture content closely, this study proposed a novel-distance based representation, by encoding the cosine distance between the essay (by learner) and essay task’s (topic and keyword) i -vectors. The learner’s essay is decoded into a word sequence, and aligned to the essay task’ topic and keyword for log-likelihood measurement. The cosine distance reveals the content elaboration score in the leaners’ essay. The mathematical equation of cosine similarity between target-reference vectors is shown in (11), assuming there are i essays and ( L i , …. L n ) and ( N i , …. N n ) are the vectors representing the learner and task’s topic and keyword respectively. The content elaboration distance between L i and N i was calculated as follows:

$\cos \left(\theta \right)=\frac{{\rm{L}}\,\cdot\, {\rm{N}}}{\left|{\rm{L}}\right|{\rm{|N|}}}=\frac{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}{N}_{i}}{\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{L}_{i}^{2}}\sqrt{\mathop{\sum }\nolimits_{i=1}^{n}{N}_{i}^{2}}}$

A high similarity value indicates a low difference between the two recognition outcomes, which in turn suggests a high level of proficiency in content elaboration.

To evaluate the effectiveness of the proposed measures in distinguishing different proficiency levels among nonnative Japanese speakers’ writing, we conducted a multi-faceted Rasch measurement analysis (Linacre, 1994 ). This approach applies measurement models to thoroughly analyze various factors that can influence test outcomes, including test takers’ proficiency, item difficulty, and rater severity, among others. The underlying principles and functionality of multi-faceted Rasch measurement are illustrated in (12).

$\log \left(\frac{{P}_{{nijk}}}{{P}_{{nij}(k-1)}}\right)={B}_{n}-{D}_{i}-{C}_{j}-{F}_{k}$

(12) defines the logarithmic transformation of the probability ratio ( P nijk /P nij(k-1) )) as a function of multiple parameters. Here, n represents the test taker, i denotes a writing proficiency measure, j corresponds to the human rater, and k represents the proficiency score. The parameter B n signifies the proficiency level of test taker n (where n ranges from 1 to N). D j represents the difficulty parameter of test item i (where i ranges from 1 to L), while C j represents the severity of rater j (where j ranges from 1 to J). Additionally, F k represents the step difficulty for a test taker to move from score ‘k-1’ to k . P nijk refers to the probability of rater j assigning score k to test taker n for test item i . P nij(k-1) represents the likelihood of test taker n being assigned score ‘k-1’ by rater j for test item i . Each facet within the test is treated as an independent parameter and estimated within the same reference framework. To evaluate the consistency of scores obtained through both human and computer analysis, we utilized the Infit mean-square statistic. This statistic is a chi-square measure divided by the degrees of freedom and is weighted with information. It demonstrates higher sensitivity to unexpected patterns in responses to items near a person’s proficiency level (Linacre, 2002 ). Fit statistics are assessed based on predefined thresholds for acceptable fit. For the Infit MNSQ, which has a mean of 1.00, different thresholds have been suggested. Some propose stricter thresholds ranging from 0.7 to 1.3 (Bond et al. 2021 ), while others suggest more lenient thresholds ranging from 0.5 to 1.5 (Eckes, 2009 ). In this study, we adopted the criterion of 0.70–1.30 for the Infit MNSQ.

Moving forward, we can now proceed to assess the effectiveness of the 16 proposed measures based on five criteria for accurately distinguishing various levels of writing proficiency among non-native Japanese speakers. To conduct this evaluation, we utilized the development dataset from the I-JAS corpus, as described in Section Dataset . Table 4 provides a measurement report that presents the performance details of the 14 metrics under consideration. The measure separation was found to be 4.02, indicating a clear differentiation among the measures. The reliability index for the measure separation was 0.891, suggesting consistency in the measurement. Similarly, the person separation reliability index was 0.802, indicating the accuracy of the assessment in distinguishing between individuals. All 16 measures demonstrated Infit mean squares within a reasonable range, ranging from 0.76 to 1.28. The Synonym overlap/paragraph (topic) measure exhibited a relatively high outfit mean square of 1.46, although the Infit mean square falls within an acceptable range. The standard error for the measures ranged from 0.13 to 0.28, indicating the precision of the estimates.

Table 5 further illustrated the weights assigned to different linguistic measures for score prediction, with higher weights indicating stronger correlations between those measures and higher scores. Specifically, the following measures exhibited higher weights compared to others: moving average type token ratio per essay has a weight of 0.0391. Mean dependency distance had a weight of 0.0388. Mean length of clause, calculated by dividing the number of words by the number of clauses, had a weight of 0.0374. Complex nominals per T-unit, calculated by dividing the number of complex nominals by the number of T-units, had a weight of 0.0379. Coordinate phrases rate, calculated by dividing the number of coordinate phrases by the number of clauses, had a weight of 0.0325. Grammatical error rate, representing the number of errors per essay, had a weight of 0.0322.

Criteria (output indicator)

The criteria used to evaluate the writing ability in this study were based on CEFR, which follows a six-point scale ranging from A1 to C2. To assess the quality of Japanese writing, the scoring criteria from Table 6 were utilized. These criteria were derived from the IELTS writing standards and served as assessment guidelines and prompts for the written output.

A prompt is a question or detailed instruction that is provided to the model to obtain a proper response. After several pilot experiments, we decided to provide the measures (Section Measures of writing proficiency for nonnative Japanese ) as the input prompt and use the criteria (Section Criteria (output indicator) ) as the output indicator. Regarding the prompt language, considering that the LLM was tasked with rating Japanese essays, would prompt in Japanese works better Footnote 5 ? We conducted experiments comparing the performance of GPT-4 using both English and Japanese prompts. Additionally, we utilized the Japanese local model OCLL with Japanese prompts. Multiple trials were conducted using the same sample. Regardless of the prompt language used, we consistently obtained the same grading results with GPT-4, which assigned a grade of B1 to the writing sample. This suggested that GPT-4 is reliable and capable of producing consistent ratings regardless of the prompt language. On the other hand, when we used Japanese prompts with the Japanese local model “OCLL”, we encountered inconsistent grading results. Out of 10 attempts with OCLL, only 6 yielded consistent grading results (B1), while the remaining 4 showed different outcomes, including A1 and B2 grades. These findings indicated that the language of the prompt was not the determining factor for reliable AES. Instead, the size of the training data and the model parameters played crucial roles in achieving consistent and reliable AES results for the language model.

The following is the utilized prompt, which details all measures and requires the LLM to score the essays using holistic and trait scores.

Please evaluate Japanese essays written by Japanese learners and assign a score to each essay on a six-point scale, ranging from A1, A2, B1, B2, C1 to C2. Additionally, please provide trait scores and display the calculation process for each trait score. The scoring should be based on the following criteria:

Moving average type-token ratio.

Number of lexical words (token) divided by the total number of words per essay.

Number of sophisticated word types divided by the total number of words per essay.

Mean length of clause.

Verb phrases per T-unit.

Clauses per T-unit.

Dependent clauses per T-unit.

Complex nominals per clause.

Adverbial clauses per clause.

Coordinate phrases per clause.

Mean dependency distance.

Synonym overlap paragraph (topic and keywords).

Word2vec cosine similarity.

Connectives per essay.

Conjunctions per essay.

Number of metadiscourse markers (types) divided by the total number of words.

Number of errors per essay.

Japanese essay text

出かける前に二人が地図を見ている間に、サンドイッチを入れたバスケットに犬が入ってしまいました。それに気づかずに二人は楽しそうに出かけて行きました。やがて突然犬がバスケットから飛び出し、二人は驚きました。バスケットの中を見ると、食べ物はすべて犬に食べられていて、二人は困ってしまいました。(ID_JJJ01_SW1)

The score of the example above was B1. Figure 3 provides an example of holistic and trait scores provided by GPT-4 (with a prompt indicating all measures) via Bing Footnote 6 .

Example of GPT-4 AES and feedback (with a prompt indicating all measures).

Statistical analysis

The aim of this study is to investigate the potential use of LLM for nonnative Japanese AES. It seeks to compare the scoring outcomes obtained from feature-based AES tools, which rely on conventional machine learning technology (i.e. Jess, JWriter), with those generated by AI-driven AES tools utilizing deep learning technology (BERT, GPT, OCLL). To assess the reliability of a computer-assisted annotation tool, the study initially established human-human agreement as the benchmark measure. Subsequently, the performance of the LLM-based method was evaluated by comparing it to human-human agreement.

To assess annotation agreement, the study employed standard measures such as precision, recall, and F-score (Brants 2000 ; Lu 2010 ), along with the quadratically weighted kappa (QWK) to evaluate the consistency and agreement in the annotation process. Assume A and B represent human annotators. When comparing the annotations of the two annotators, the following results are obtained. The evaluation of precision, recall, and F-score metrics was illustrated in equations (13) to (15).

${\rm{Recall}}(A,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,A}$

${\rm{Precision}}(A,\,B)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{identical}}\,{\rm{nodes}}\,{\rm{in}}\,A\,{\rm{and}}\,B}{{\rm{Number}}\,{\rm{of}}\,{\rm{nodes}}\,{\rm{in}}\,B}$

The F-score is the harmonic mean of recall and precision:

${\rm{F}}-{\rm{score}}=\frac{2* ({\rm{Precision}}* {\rm{Recall}})}{{\rm{Precision}}+{\rm{Recall}}}$

The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero.

In accordance with Taghipour and Ng ( 2016 ), the calculation of QWK involves two steps:

Step 1: Construct a weight matrix W as follows:

${W}_{{ij}}=\frac{{(i-j)}^{2}}{{(N-1)}^{2}}$

i represents the annotation made by the tool, while j represents the annotation made by a human rater. N denotes the total number of possible annotations. Matrix O is subsequently computed, where O_( i, j ) represents the count of data annotated by the tool ( i ) and the human annotator ( j ). On the other hand, E refers to the expected count matrix, which undergoes normalization to ensure that the sum of elements in E matches the sum of elements in O.

Step 2: With matrices O and E, the QWK is obtained as follows:

K = 1- $\frac{\sum i,j{W}_{i,j}\,{O}_{i,j}}{\sum i,j{W}_{i,j}\,{E}_{i,j}}$

The value of the quadratic weighted kappa increases as the level of agreement improves. Further, to assess the accuracy of LLM scoring, the proportional reductive mean square error (PRMSE) was employed. The PRMSE approach takes into account the variability observed in human ratings to estimate the rater error, which is then subtracted from the variance of the human labels. This calculation provides an overall measure of agreement between the automated scores and true scores (Haberman et al. 2015 ; Loukina et al. 2020 ; Taghipour and Ng, 2016 ). The computation of PRMSE involves the following steps:

Step 1: Calculate the mean squared errors (MSEs) for the scoring outcomes of the computer-assisted tool (MSE tool) and the human scoring outcomes (MSE human).

Step 2: Determine the PRMSE by comparing the MSE of the computer-assisted tool (MSE tool) with the MSE from human raters (MSE human), using the following formula:

${\rm{PRMSE}}=1-\frac{({\rm{MSE}}\,{\rm{tool}})\,}{({\rm{MSE}}\,{\rm{human}})\,}=1-\,\frac{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-{\hat{{\rm{y}}}}_{{\rm{i}}})}^{2}}{{\sum }_{i}^{n}=1{({{\rm{y}}}_{i}-\hat{{\rm{y}}})}^{2}}$

In the numerator, ŷi represents the scoring outcome predicted by a specific LLM-driven AES system for a given sample. The term y i − ŷ i represents the difference between this predicted outcome and the mean value of all LLM-driven AES systems’ scoring outcomes. It quantifies the deviation of the specific LLM-driven AES system’s prediction from the average prediction of all LLM-driven AES systems. In the denominator, y i − ŷ represents the difference between the scoring outcome provided by a specific human rater for a given sample and the mean value of all human raters’ scoring outcomes. It measures the discrepancy between the specific human rater’s score and the average score given by all human raters. The PRMSE is then calculated by subtracting the ratio of the MSE tool to the MSE human from 1. PRMSE falls within the range of 0 to 1, with larger values indicating reduced errors in LLM’s scoring compared to those of human raters. In other words, a higher PRMSE implies that LLM’s scoring demonstrates greater accuracy in predicting the true scores (Loukina et al. 2020 ). The interpretation of kappa values, ranging from 0 to 1, is based on the work of Landis and Koch ( 1977 ). Specifically, the following categories are assigned to different ranges of kappa values: −1 indicates complete inconsistency, 0 indicates random agreement, 0.0 ~ 0.20 indicates extremely low level of agreement (slight), 0.21 ~ 0.40 indicates moderate level of agreement (fair), 0.41 ~ 0.60 indicates medium level of agreement (moderate), 0.61 ~ 0.80 indicates high level of agreement (substantial), 0.81 ~ 1 indicates almost perfect level of agreement. All statistical analyses were executed using Python script.

Results and discussion

Annotation reliability of the llm.

This section focuses on assessing the reliability of the LLM’s annotation and scoring capabilities. To evaluate the reliability, several tests were conducted simultaneously, aiming to achieve the following objectives:

Assess the LLM’s ability to differentiate between test takers with varying levels of oral proficiency.

Determine the level of agreement between the annotations and scoring performed by the LLM and those done by human raters.

The evaluation of the results encompassed several metrics, including: precision, recall, F-Score, quadratically-weighted kappa, proportional reduction of mean squared error, Pearson correlation, and multi-faceted Rasch measurement.

Inter-annotator agreement (human–human annotator agreement)

We started with an agreement test of the two human annotators. Two trained annotators were recruited to determine the writing task data measures. A total of 714 scripts, as the test data, was utilized. Each analysis lasted 300–360 min. Inter-annotator agreement was evaluated using the standard measures of precision, recall, and F-score and QWK. Table 7 presents the inter-annotator agreement for the various indicators. As shown, the inter-annotator agreement was fairly high, with F-scores ranging from 1.0 for sentence and word number to 0.666 for grammatical errors.

The findings from the QWK analysis provided further confirmation of the inter-annotator agreement. The QWK values covered a range from 0.950 ( p = 0.000) for sentence and word number to 0.695 for synonym overlap number (keyword) and grammatical errors ( p = 0.001).

Agreement of annotation outcomes between human and LLM

To evaluate the consistency between human annotators and LLM annotators (BERT, GPT, OCLL) across the indices, the same test was conducted. The results of the inter-annotator agreement (F-score) between LLM and human annotation are provided in Appendix B-D. The F-scores ranged from 0.706 for Grammatical error # for OCLL-human to a perfect 1.000 for GPT-human, for sentences, clauses, T-units, and words. These findings were further supported by the QWK analysis, which showed agreement levels ranging from 0.807 ( p = 0.001) for metadiscourse markers for OCLL-human to 0.962 for words ( p = 0.000) for GPT-human. The findings demonstrated that the LLM annotation achieved a significant level of accuracy in identifying measurement units and counts.

Reliability of LLM-driven AES’s scoring and discriminating proficiency levels

This section examines the reliability of the LLM-driven AES scoring through a comparison of the scoring outcomes produced by human raters and the LLM ( Reliability of LLM-driven AES scoring ). It also assesses the effectiveness of the LLM-based AES system in differentiating participants with varying proficiency levels ( Reliability of LLM-driven AES discriminating proficiency levels ).

Reliability of LLM-driven AES scoring

Table 8 summarizes the QWK coefficient analysis between the scores computed by the human raters and the GPT-4 for the individual essays from I-JAS Footnote 7 . As shown, the QWK of all measures ranged from k = 0.819 for lexical density (number of lexical words (tokens)/number of words per essay) to k = 0.644 for word2vec cosine similarity. Table 9 further presents the Pearson correlations between the 16 writing proficiency measures scored by human raters and GPT 4 for the individual essays. The correlations ranged from 0.672 for syntactic complexity to 0.734 for grammatical accuracy. The correlations between the writing proficiency scores assigned by human raters and the BERT-based AES system were found to range from 0.661 for syntactic complexity to 0.713 for grammatical accuracy. The correlations between the writing proficiency scores given by human raters and the OCLL-based AES system ranged from 0.654 for cohesion to 0.721 for grammatical accuracy. These findings indicated an alignment between the assessments made by human raters and both the BERT-based and OCLL-based AES systems in terms of various aspects of writing proficiency.

Reliability of LLM-driven AES discriminating proficiency levels

After validating the reliability of the LLM’s annotation and scoring, the subsequent objective was to evaluate its ability to distinguish between various proficiency levels. For this analysis, a dataset of 686 individual essays was utilized. Table 10 presents a sample of the results, summarizing the means, standard deviations, and the outcomes of the one-way ANOVAs based on the measures assessed by the GPT-4 model. A post hoc multiple comparison test, specifically the Bonferroni test, was conducted to identify any potential differences between pairs of levels.

As the results reveal, seven measures presented linear upward or downward progress across the three proficiency levels. These were marked in bold in Table 10 and comprise one measure of lexical richness, i.e. MATTR (lexical diversity); four measures of syntactic complexity, i.e. MDD (mean dependency distance), MLC (mean length of clause), CNT (complex nominals per T-unit), CPC (coordinate phrases rate); one cohesion measure, i.e. word2vec cosine similarity and GER (grammatical error rate). Regarding the ability of the sixteen measures to distinguish adjacent proficiency levels, the Bonferroni tests indicated that statistically significant differences exist between the primary level and the intermediate level for MLC and GER. One measure of lexical richness, namely LD, along with three measures of syntactic complexity (VPT, CT, DCT, ACC), two measures of cohesion (SOPT, SOPK), and one measure of content elaboration (IMM), exhibited statistically significant differences between proficiency levels. However, these differences did not demonstrate a linear progression between adjacent proficiency levels. No significant difference was observed in lexical sophistication between proficiency levels.

To summarize, our study aimed to evaluate the reliability and differentiation capabilities of the LLM-driven AES method. For the first objective, we assessed the LLM’s ability to differentiate between test takers with varying levels of oral proficiency using precision, recall, F-Score, and quadratically-weighted kappa. Regarding the second objective, we compared the scoring outcomes generated by human raters and the LLM to determine the level of agreement. We employed quadratically-weighted kappa and Pearson correlations to compare the 16 writing proficiency measures for the individual essays. The results confirmed the feasibility of using the LLM for annotation and scoring in AES for nonnative Japanese. As a result, Research Question 1 has been addressed.

Comparison of BERT-, GPT-, OCLL-based AES, and linguistic-feature-based computation methods

This section aims to compare the effectiveness of five AES methods for nonnative Japanese writing, i.e. LLM-driven approaches utilizing BERT, GPT, and OCLL, linguistic feature-based approaches using Jess and JWriter. The comparison was conducted by comparing the ratings obtained from each approach with human ratings. All ratings were derived from the dataset introduced in Dataset . To facilitate the comparison, the agreement between the automated methods and human ratings was assessed using QWK and PRMSE. The performance of each approach was summarized in Table 11 .

The QWK coefficient values indicate that LLMs (GPT, BERT, OCLL) and human rating outcomes demonstrated higher agreement compared to feature-based AES methods (Jess and JWriter) in assessing writing proficiency criteria, including lexical richness, syntactic complexity, content, and grammatical accuracy. Among the LLMs, the GPT-4 driven AES and human rating outcomes showed the highest agreement in all criteria, except for syntactic complexity. The PRMSE values suggest that the GPT-based method outperformed linguistic feature-based methods and other LLM-based approaches. Moreover, an interesting finding emerged during the study: the agreement coefficient between GPT-4 and human scoring was even higher than the agreement between different human raters themselves. This discovery highlights the advantage of GPT-based AES over human rating. Ratings involve a series of processes, including reading the learners’ writing, evaluating the content and language, and assigning scores. Within this chain of processes, various biases can be introduced, stemming from factors such as rater biases, test design, and rating scales. These biases can impact the consistency and objectivity of human ratings. GPT-based AES may benefit from its ability to apply consistent and objective evaluation criteria. By prompting the GPT model with detailed writing scoring rubrics and linguistic features, potential biases in human ratings can be mitigated. The model follows a predefined set of guidelines and does not possess the same subjective biases that human raters may exhibit. This standardization in the evaluation process contributes to the higher agreement observed between GPT-4 and human scoring. Section Prompt strategy of the study delves further into the role of prompts in the application of LLMs to AES. It explores how the choice and implementation of prompts can impact the performance and reliability of LLM-based AES methods. Furthermore, it is important to acknowledge the strengths of the local model, i.e. the Japanese local model OCLL, which excels in processing certain idiomatic expressions. Nevertheless, our analysis indicated that GPT-4 surpasses local models in AES. This superior performance can be attributed to the larger parameter size of GPT-4, estimated to be between 500 billion and 1 trillion, which exceeds the sizes of both BERT and the local model OCLL.

Prompt strategy

In the context of prompt strategy, Mizumoto and Eguchi ( 2023 ) conducted a study where they applied the GPT-3 model to automatically score English essays in the TOEFL test. They found that the accuracy of the GPT model alone was moderate to fair. However, when they incorporated linguistic measures such as cohesion, syntactic complexity, and lexical features alongside the GPT model, the accuracy significantly improved. This highlights the importance of prompt engineering and providing the model with specific instructions to enhance its performance. In this study, a similar approach was taken to optimize the performance of LLMs. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. Model 1 was used as the baseline, representing GPT-4 without any additional prompting. Model 2, on the other hand, involved GPT-4 prompted with 16 measures that included scoring criteria, efficient linguistic features for writing assessment, and detailed measurement units and calculation formulas. The remaining models (Models 3 to 18) utilized GPT-4 prompted with individual measures. The performance of these 18 different models was assessed using the output indicators described in Section Criteria (output indicator) . By comparing the performances of these models, the study aimed to understand the impact of prompt engineering on the accuracy and effectiveness of GPT-4 in AES tasks.

Based on the PRMSE scores presented in Fig. 4 , it was observed that Model 1, representing GPT-4 without any additional prompting, achieved a fair level of performance. However, Model 2, which utilized GPT-4 prompted with all measures, outperformed all other models in terms of PRMSE score, achieving a score of 0.681. These results indicate that the inclusion of specific measures and prompts significantly enhanced the performance of GPT-4 in AES. Among the measures, syntactic complexity was found to play a particularly significant role in improving the accuracy of GPT-4 in assessing writing quality. Following that, lexical diversity emerged as another important factor contributing to the model’s effectiveness. The study suggests that a well-prompted GPT-4 can serve as a valuable tool to support human assessors in evaluating writing quality. By utilizing GPT-4 as an automated scoring tool, the evaluation biases associated with human raters can be minimized. This has the potential to empower teachers by allowing them to focus on designing writing tasks and guiding writing strategies, while leveraging the capabilities of GPT-4 for efficient and reliable scoring.

PRMSE scores of the 18 AES models.

This study aimed to investigate two main research questions: the feasibility of utilizing LLMs for AES and the impact of prompt engineering on the application of LLMs in AES.

To address the first objective, the study compared the effectiveness of five different models: GPT, BERT, the Japanese local LLM (OCLL), and two conventional machine learning-based AES tools (Jess and JWriter). The PRMSE values indicated that the GPT-4-based method outperformed other LLMs (BERT, OCLL) and linguistic feature-based computational methods (Jess and JWriter) across various writing proficiency criteria. Furthermore, the agreement coefficient between GPT-4 and human scoring surpassed the agreement among human raters themselves, highlighting the potential of using the GPT-4 tool to enhance AES by reducing biases and subjectivity, saving time, labor, and cost, and providing valuable feedback for self-study. Regarding the second goal, the role of prompt design was investigated by comparing 18 models, including a baseline model, a model prompted with all measures, and 16 models prompted with one measure at a time. GPT-4, which outperformed BERT and OCLL, was selected as the candidate model. The PRMSE scores of the models showed that GPT-4 prompted with all measures achieved the best performance, surpassing the baseline and other models.

In conclusion, this study has demonstrated the potential of LLMs in supporting human rating in assessments. By incorporating automation, we can save time and resources while reducing biases and subjectivity inherent in human rating processes. Automated language assessments offer the advantage of accessibility, providing equal opportunities and economic feasibility for individuals who lack access to traditional assessment centers or necessary resources. LLM-based language assessments provide valuable feedback and support to learners, aiding in the enhancement of their language proficiency and the achievement of their goals. This personalized feedback can cater to individual learner needs, facilitating a more tailored and effective language-learning experience.

There are three important areas that merit further exploration. First, prompt engineering requires attention to ensure optimal performance of LLM-based AES across different language types. This study revealed that GPT-4, when prompted with all measures, outperformed models prompted with fewer measures. Therefore, investigating and refining prompt strategies can enhance the effectiveness of LLMs in automated language assessments. Second, it is crucial to explore the application of LLMs in second-language assessment and learning for oral proficiency, as well as their potential in under-resourced languages. Recent advancements in self-supervised machine learning techniques have significantly improved automatic speech recognition (ASR) systems, opening up new possibilities for creating reliable ASR systems, particularly for under-resourced languages with limited data. However, challenges persist in the field of ASR. First, ASR assumes correct word pronunciation for automatic pronunciation evaluation, which proves challenging for learners in the early stages of language acquisition due to diverse accents influenced by their native languages. Accurately segmenting short words becomes problematic in such cases. Second, developing precise audio-text transcriptions for languages with non-native accented speech poses a formidable task. Last, assessing oral proficiency levels involves capturing various linguistic features, including fluency, pronunciation, accuracy, and complexity, which are not easily captured by current NLP technology.

Data availability

The dataset utilized was obtained from the International Corpus of Japanese as a Second Language (I-JAS). The data URLs: [ https://www2.ninjal.ac.jp/jll/lsaj/ihome2.html ].

J-CAT and TTBJ are two computerized adaptive tests used to assess Japanese language proficiency.

SPOT is a specific component of the TTBJ test.

J-CAT: https://www.j-cat2.org/html/ja/pages/interpret.html

SPOT: https://ttbj.cegloc.tsukuba.ac.jp/p1.html#SPOT .

The study utilized a prompt-based GPT-4 model, developed by OpenAI, which has an impressive architecture with 1.8 trillion parameters across 120 layers. GPT-4 was trained on a vast dataset of 13 trillion tokens, using two stages: initial training on internet text datasets to predict the next token, and subsequent fine-tuning through reinforcement learning from human feedback.

https://www2.ninjal.ac.jp/jll/lsaj/ihome2-en.html .

http://jhlee.sakura.ne.jp/JEV/ by Japanese Learning Dictionary Support Group 2015.

We express our sincere gratitude to the reviewer for bringing this matter to our attention.

On February 7, 2023, Microsoft began rolling out a major overhaul to Bing that included a new chatbot feature based on OpenAI’s GPT-4 (Bing.com).

Appendix E-F present the analysis results of the QWK coefficient between the scores computed by the human raters and the BERT, OCLL models.

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This research was funded by National Foundation of Social Sciences (22BYY186) to Wenchao Li.

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Li, W., Liu, H. Applying large language models for automated essay scoring for non-native Japanese. Humanit Soc Sci Commun 11 , 723 (2024). https://doi.org/10.1057/s41599-024-03209-9

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पर्यावरण पर संस्कृत में निबंध। Essay on Environment in Sanskrit
पर्यावरण पर संस्कृत में निबंध। Essay on Environment in Sanskrit : वयं ...
पर्यावरणम्
पर्यावरणम् | Environment Essay in Sanskrit "स्वल्यं तथायुर्बहुवश्च विनाः ...
PDF Origin of Environmental Science From Vedas
They studied nature's drama very minutely. Sand-storm and cyclone, intense lightening, terrific thunderclaps, the heavy rush of rain in monsoon, the swift flood in the stream that comes down from the hills, the scorching heat of the sun, the cracking red flames of the fire, all witness to power beyond man's power.
PDF Environment: Sanskrit literature and Bhartr̥hari's Śatakatrayam: A
Abstract. Nature is crashing today due to the perversion of men and men made machines. Mega fauna has lost their natural power of development along with the destruction of nature. Awareness of the environment can be seen in Sanskrit literature. In ancient times, the people and nature of India lived as one. In the Vedic literature, the worship ...
World Environment Day in Sanskrit
June 4, 2024. Sanskrit and its literature have always respected and celebrated the environment. Take this Shloka from Atharvaveda as an example; it establishes a sacred connection between mother nature and us (humans), setting a very deep precedent for how we should be treating our fellow earthlings. Happy Environment Day to you!
The Past Teaching the Present: Ancient Sanskrit Texts Discuss the
The five elements of nature and the human body (earth, air, fire, water and ether/space) interconnect according to the Hindu faith. ... For example, the Sanskrit Jatakamala is a collection of tales regarding the past lives of the Buddha. Of the 34 tales, the Buddha is reincarnated as an animal, a bird, or a fish a total of 14 times.
PDF Environmental Concern in Sanskrit Text: a Literary Survey
The Vedas, Upaniṣads, Purāṇas, Epics and even Sanskrit dramas contained a rich body of literature which are very much relevant to understand the importance of nature and its protection. This paper tries to find out the knowledge related to environment and its protection as is mentioned in writings of Sanskrit literature.
Prakṛti
Prakriti (Sanskrit: प्रकृति IAST: Prakṛti) is "the original or natural form or condition of anything, original or primary substance". It is a key concept in Hinduism, formulated by its Sāṅkhya school, where it does not refer to matter or nature, but "includes all the cognitive, moral, psychological, emotional, sensorial and physical aspects of reality", stressing "Prakṛti ...
Nature related words in Sanskrit to English
nature related Sanskrit words, Sanskrit word for nature, Sanskrit word for environment, nature related Sanskrit words, Sanskrit word for natural
An Essay On The Nature, Age, And Origin Of The Sanskrit Writing And
""An Essay On The Nature, Age, And Origin Of The Sanskrit Writing And Language"" is a scholarly work written by Charles William Wall and originally published in 1838. The book explores the history and development of the Sanskrit language, which is one of the oldest and most important languages in the world.
Sanskrit
Rigveda 10.71.1-4 Translated by Roger Woodard The Vedic Sanskrit found in the Ṛg-veda is distinctly more archaic than other Vedic texts, and in many respects, the Rigvedic language is notably more similar to those found in the archaic texts of Old Avestan Zoroastrian Gathas and Homer's Iliad and Odyssey. According to Stephanie W. Jamison and Joel P. Brereton - Indologists known for their ...
प्रकृति पर संस्कृत श्लोक हिंदी अर्थ सहित
Sanskrit Shlokas on Nature with Hindi Meaning: यहाँ पर प्रकृति पर संस्कृत श्लोक लिखे है। साथ में इनका हिंदी अर्थ भी सरल भाषा में लिखा है।
Sanskrit language
Sanskrit language, (from Sanskrit saṃskṛta, "adorned, cultivated, purified"), an Old Indo-Aryan language in which the most ancient documents are the Vedas, composed in what is called Vedic Sanskrit.Although Vedic documents represent the dialects then found in the northern midlands of the Indian subcontinent and areas immediately east thereof, the very earliest texts—including the ...
पर्यावरणम् संस्कृत निबन्ध
पर्यावरणम् संस्कृत निबन्ध | environment essay in Sanskrit | पर्यावरण पर निबंध संस्कृत में# ...
essay about nature in sanskrit
पर्यावरण पर संस्कृत मे निबंध: | Essay on Save Environment In Sanskrit. इस धरती पर रहने वाली सभ
Nature Essay In Sanskrit
Essay writing services are legal if the company has passed a number of necessary checks and is licensed. This area is well developed and regularly monitored by serious services. ... Nature Essay In Sanskrit, Conceptual Paradigm Thesis Sample, Lesson 7 Homework 4.3, Hardships In Life Essay, Ap Language Composition Synthesis Essay Example, Walter ...
Applying large language models for automated essay scoring for ...
Recent advancements in artificial intelligence (AI) have led to an increased use of large language models (LLMs) for language assessment tasks such as automated essay scoring (AES), automated ...
Nature Essay In Sanskrit
Nature Essay In Sanskrit: 4.8/5. ID 173. Benny. Gain recognition with the help of my essay writer. Generally, our writers, who will write my essay for me, have the responsibility to show their determination in writing the essay for you, but there is more they can do. They can ease your admission process for higher education and write various ...
Nature Essay In Sanskrit
Nature Essay In Sanskrit. Download Once the deadline is over, we will upload your order into you personal profile and send you a copy to the email address you used while placing order. The first step in making your write my essay request is filling out a 10-minute order form. Submit the instructions, desired sources, and deadline.
Nature Essay In Sanskrit
Nature Essay In Sanskrit: Nursing Business and Economics Management Marketing +130. GOT IT. Level: Master's, University, College, PHD, High School, Undergraduate, Professional. Emilie Nilsson #11 in Global Rating 10 Customer reviews. Toll free 24/7 +1-323-996-2024 296 ...
Essay On Nature In Sanskrit
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पर्यावरणम् | Environment Essay in Sanskrit

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World Environment Day in Sanskrit

प्रकृतिरेव शरणम्।

संरक्षेद्दूषितो न स्याल्लोकः मानवजीवनम्।

माता भूमिः पुत्रोऽहं पृथिव्याः।

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शैले शैले न माणिक्यं मौतिकं न गजे गजे । साधवो नहि सर्वत्र चन्दनं न वने वने ॥

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संरक्षेद्दूषितो न स्याल्लोकः मानवजीवनम्।

माता भूमिः पुत्रोऽहं पृथिव्याः।

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शैले शैले न माणिक्यं मौतिकं न गजे गजे । साधवो नहि सर्वत्र चन्दनं न वने वने ॥

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पर्यावरण संस्कृत निबंध- Essay on Environment in Sanskrit

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