Exploring the Transformative Influence of Artificial Intelligence on Dharmic Studies
Dr. Devinder Pal Singh
(Image: Courtesy Gemini)
Abstract
Artificial Intelligence (AI) is rapidly transforming the humanities by introducing new methods for textual analysis, translation, and classification. Besides, it provides invaluable help with knowledge retrieval, digital preservation, and comparative research. Dharmic Studies encompasses the intellectual, philosophical, ethical, religious, linguistic, and cultural traditions associated particularly with Hinduism, Buddhism, Jainism, and Sikhism. This field stands to benefit substantially from these developments. Dharmic traditions possess enormous multilingual textual corpora and complex commentarial traditions. They also encompass diverse philosophical vocabularies, manuscript cultures, oral traditions, and historically layered interpretive practices. AI can assist scholars in organizing these materials and identifying conceptual patterns across them. It can also support the comparison of textual traditions, reconstruction of damaged manuscripts, improved accessibility, and the generation of new research questions. Emerging research studies have demonstrated the potential of machine learning techniques to identify thematic relationships among major dharmic and philosophical texts. However, AI also introduces significant epistemological, ethical, cultural, and methodological challenges. Algorithmic bias and inadequate representation of Indic languages can distort Dharmic knowledge. Hallucinated interpretations, decontextualization, textual reductionism, intellectual-property concerns, and the tendency to privilege dominant digital sources can further compound these challenges. This article examines AI’s transformative potential in Dharmic Studies. It argues for a human-centred, source-critical, culturally grounded, and ethically accountable model. AI should augment, rather than replace, philological expertise and lived tradition. It should also support interpretive communities and critical scholarship.
Keywords: Artificial Intelligence, Dharmic Studies, Hinduism, Buddhism, Jainism, Sikhism, Digital Humanities, Generative AI, Religious Studies, Computational Humanities
1. Introduction: From Digital Humanities to AI-Enabled Dharmic Studies
The emergence of Artificial Intelligence (AI), particularly machine learning, natural language processing (NLP), computer vision, and generative AI, represents a significant methodological development for the humanities (Arrieta et al., 2020). Unlike earlier digitization projects that primarily converted physical texts into searchable electronic formats, AI can analyze relationships within enormous bodies of information. Moreover, it can assist researchers in identifying patterns that may be difficult to detect through conventional reading alone. UNESCO (2023) nevertheless emphasizes that AI should be integrated into education and research within a human-centred framework. Such a framework should safeguard cultural diversity, privacy, intellectual agency, and academic integrity (Miao & Holmes, 2023).
Dharmic Studies provides an especially fertile field for such experimentation (Singler, 2024). Hindu, Buddhist, Jain, and Sikh traditions encompass extensive textual and oral corpora. These corpora extend across Sanskrit, Pali, Prakrit, Ardhamagadhi, Apabhraṃśa, Tamil, Punjabi, Hindi, Bengali, Tibetan, Chinese, Persian, and numerous other languages. Their intellectual histories also encompass multiple schools, commentarial traditions, debates, and translations. They further include manuscript variants, philosophical systems, and diverse regional interpretations. Therefore, conventional scholarship can be constrained by the enormous scale and linguistic diversity of the material.
AI can potentially transform this situation by enabling researchers to search, classify, compare, translate, visualize, and analyze large corpora. Topic modelling, for example, has already been applied to Hindu philosophical literature. Chandra and Ranjan (2022) used BERT-based methods to investigate thematic relationships between the Upanishads and the Bhagavad Gita. Their study demonstrated how computational methods can supplement traditional textual interpretation (Chandra & Ranjan, 2022).
Yet transformation should not be confused with replacement. Religious texts are not merely datasets. Their meanings emerge through language, history, commentary, practice, community, ritual, philosophy, and lived experience. AI therefore presents Dharmic Studies with an extraordinary research opportunity (Smith, 2024). However, it also raises fundamental methodological challenges: How can computational intelligence expand scholarship without reducing Dharmic knowledge to computationally measurable information?
2. Research Methodology
This study employs a qualitative, interdisciplinary, and exploratory research methodology to examine the transformative influence of AI on Dharmic Studies. The research adopts a comparative approach encompassing Hinduism, Buddhism, Jainism, and Sikhism. It pays particular attention to their textual, philosophical, linguistic, ethical, and cultural dimensions. Primary sources include selected canonical and philosophical texts, such as the Vedas and Upanishads, the Bhagavad Gita, Buddhist scriptures, Jain literature, and Sri Guru Granth Sahib. Secondary sources comprise peer-reviewed academic publications, scholarly monographs, digital humanities research, and authoritative reports on AI and education. The study also incorporates conceptual analysis to examine the potential of AI-assisted textual analysis, natural-language processing, and machine translation for the study of Dharmic traditions. It further considers how manuscript digitization, computational comparison, and generative AI may influence this field of study.
A critical literature review method is employed to identify AI-related opportunities. It also examines limitations related to algorithmic bias, hallucination, cultural reductionism, linguistic inequity, and questions of epistemic authority. The analysis is guided by a human-centred and source-critical framework. It emphasizes transparency, contextual interpretation, cultural sensitivity, and scholarly verification. Rather than treating AI as an autonomous interpretive authority, the methodology conceptualizes it as an analytical instrument that can complement humanistic and philological scholarship. However, AI is not regarded as a replacement for human judgment, expertise, or rigorous scholarly inquiry.
3. Conceptual Foundations: Understanding AI within Dharmic Studies
AI should first be understood as a collection of computational techniques rather than as an independent source of knowledge. Machine-learning systems identify patterns from training data. NLP systems process linguistic information. Large language models generate statistically probable sequences of language. Computer-vision systems analyze visual patterns. Moreover, retrieval-augmented systems connect generative models with external databases. These capabilities can be extremely valuable for scholarship. However, they do not automatically constitute understanding in the philosophical or hermeneutical sense (Singler, 2024; Boddington, 2023).
This distinction is particularly important in Dharmic Studies. Concepts such as dharma, karma, moksha, nirvana, ahimsa, seva, hukam, naam, anatman, and tattva carry historically layered meanings. These terms cannot always be translated into equivalent English concepts. A computational system may identify occurrences of a term and establish statistical relationships. However, determining its philosophical significance requires historical and interpretive knowledge.
The challenge becomes more complicated because AI systems inherit patterns from their training data. Research has demonstrated that machine-learning systems can reproduce social and cultural biases embedded in linguistic corpora (Caliskan et al., 2017). Prabhakaran et al. (2022) similarly argue that AI systems can exhibit cultural incongruities because their development and training often reflect limited geographic and cultural contexts. This is particularly significant for Dharmic Studies because much Indic knowledge remains unevenly represented in digitally accessible datasets.
Accordingly, AI should be positioned as a research collaborator and analytical instrument, not an autonomous authority on Dharmic truth. Its strongest contribution may lie in expanding the scale and speed of scholarly investigation. However, interpretation, contextualization, validation, and normative judgment should be left to qualified human scholars and communities.
4. AI and the Transformation of Dharmic Textual Scholarship
One of the most significant applications of AI in Dharmic Studies is computational textual analysis. Traditional scholarship often requires researchers to work painstakingly through manuscripts, editions, translations, and commentaries. It also requires careful engagement with secondary literature. AI can assist by rapidly identifying recurring terms, conceptual associations, and stylistic patterns across large corpora. It can also identify textual parallels and variations.
4.1 Digital Preservation and Manuscript Studies
Many Dharmic traditions possess extensive manuscript collections. The physical preservation of these manuscripts is threatened by deterioration, environmental conditions, limited institutional resources, and restricted accessibility. Digitization provides an important mechanism for preserving these valuable materials. AI-based optical character recognition and handwriting recognition can potentially make digitized manuscripts computationally searchable.
Computer vision can assist in identifying damaged characters and reconstructing probable textual sequences. It can also help classify manuscripts and compare different textual witnesses. Such capabilities could be particularly valuable for Sanskrit, Pali, Prakrit, Gurmukhi, Tibetan, and other scripts. The historical forms of these scripts may present difficulties for conventional OCR systems.
However, computational reconstruction must remain probabilistic rather than authoritative. An AI-generated restoration of a damaged manuscript represents a hypothesis rather than the original text. Scholarly editions must therefore clearly distinguish between manuscript evidence and machine-generated reconstructions.
AI can also facilitate intertextual discovery. A researcher studying a particular philosophical concept could search thousands of texts for linguistic parallels. The researcher could also identify conceptual parallels across different texts. Rather than replacing close reading, this approach would help scholars identify previously unnoticed relationships. These relationships could then be investigated through conventional philological methods.
Such an approach could transform Dharmic textual scholarship (Singh, 2023b-c; Singh, 2024a-b). It could move scholarship beyond predominantly sequential reading. It would instead promote a complementary model that combines close reading and distant reading. The objective would not be to make scholarship less human, but to enable scholars to ask larger and more sophisticated questions.
5. AI, Translation, and the Accessibility of Dharmic Knowledge
Translation represents another transformative area. Dharmic traditions have historically travelled across linguistic boundaries through translation and commentary. AI-based translation systems can accelerate this process. This can be done by making texts available to audiences who lack proficiency in their original languages.
This opportunity is particularly significant because Dharmic literature contains concepts for which direct lexical equivalents may not exist in modern English or other languages. AI can generate preliminary translations and compare multiple translations. It can also identify divergent renderings and explain possible semantic fields. Such tools could therefore function as research assistants for students and scholars.
However, automated translation presents substantial risks. A translation system trained primarily on modern or secondary sources may reproduce conventional interpretations. It may overlook alternative meanings (Doshi, 2024). It may also translate a technical philosophical term according to its most statistically common contemporary meaning rather than its historical usage. In religious studies, such errors can alter the philosophical argument of an entire passage.
For example, translating dharma simply as “religion,” karma as “fate,” or nirvana as “heaven” would create conceptual distortions. Similarly, Sikh concepts such as hukam, haumai, nadar, and naam require contextual interpretation rather than dictionary-level substitution Singh, 2023b-c).
AI translation should therefore follow a human-in-the-loop model. Scholars with linguistic and disciplinary expertise should verify generated translations against primary texts and grammatical structures. They must also confirm historical context and established commentarial traditions. AI can increase accessibility. However, accessibility without accuracy can produce large-scale misinformation.
The goal should consequently not be “AI translation replacing scholars.” Instead, it should be AI-assisted multilingual scholarship. In this approach, computational systems generate possible translations and interpretations, while human experts determine which interpretations are academically defensible.
6. AI and Comparative Study of Hinduism, Buddhism, Jainism, and Sikhism
AI offers particularly promising possibilities for comparative Dharmic Studies (Pandya, 2024; Zheng, 2024). Conventional comparative religion frequently depends upon carefully selected texts and concepts. Computational methods could broaden this process by examining large corpora across traditions. Moreover, these methods can identify similarities, differences, and conceptual networks.
For example, AI could map the occurrence of concepts concerning ethics, liberation, suffering, consciousness, compassion, nonviolence, duty, selfhood, attachment, equality, and transcendence. It could also examine the contextual relationships among these concepts across different philosophical and religious traditions. Such computational mapping could reveal patterns that invite further philosophical investigation.
However, comparative analysis requires methodological caution. Similar vocabulary does not necessarily indicate identical philosophical meaning. Concepts such as karma appear across several Dharmic traditions. However, their precise meanings differ considerably. Likewise, concepts of liberation in Hindu philosophical schools, Buddhism, Jainism, and Sikhism cannot be treated as interchangeable. Although these traditions address human suffering and spiritual transformation, their understandings of liberation differ significantly.
AI, therefore, has the greatest value when it identifies questions rather than prematurely supplying conclusions. A computational model might reveal that two traditions frequently associate certain concepts. However, to determine why those associations occur requires contextual and historical scholarship.
This approach also challenges simplistic narratives of either absolute similarity or absolute difference. AI-supported corpus analysis could help scholars investigate intellectual continuities and historical interactions with greater precision. It could also facilitate the study of borrowings, transformations, and conceptual divergences.
Chandra and Ranjan’s (2022) computational comparison of the Upanishads and Bhagavad Gita illustrates this possibility. Their findings suggest substantial thematic overlap between the corpora. They also demonstrate that computational similarity can serve as a starting point for philosophical inquiry. It should not be regarded as the endpoint of such inquiry. Thus, AI could encourage a more systematic approach to comparative Dharmic scholarship. At the same time, it could help preserve the distinctiveness of individual traditions.
7. AI and the Study of Dharmic Philosophy
The philosophical traditions of Hinduism, Buddhism, Jainism, and Sikhism contain sophisticated discussions of consciousness and knowledge. They also explore causality, ethics, and liberation. In addition, these traditions examine language, perception, selfhood, and reality. AI creates new possibilities for studying these philosophical systems (Chandra & Ranjan, 2022; Bajracharya, 2024; Singler, 2024; Singh, 2023b-c; Singh, 2024c). It can support research through computational modelling and conceptual analysis.
One potential application is philosophical concept mapping. AI can identify relationships among terms across extensive philosophical corpora. It can also visualize these relationships as conceptual networks. Researchers could investigate how particular concepts develop across different texts and schools. They could further examine their evolution across historical periods and commentaries.
AI can also assist in comparing philosophical arguments. A researcher might use computational tools to identify relevant passages. These passages may deal with questions about the nature of the self. They may also examine the relationship between action and consequence. Other passages may address the possibility of liberation. Some may focus on the sources of valid knowledge. Computational systems can help organize this relevant evidence. Such organization can be useful before detailed philosophical analysis begins.
Yet computational representation creates a philosophical danger. One danger is the assumption that what can be quantified is necessarily what matters. Dharmic philosophies frequently distinguish intellectual information from transformative knowledge. Knowledge, in several Dharmic traditions, involves more than possessing propositions. It may also involve a transformation of consciousness, conduct, perception, or attachment.
This distinction has implications for AI itself. An AI system can reproduce descriptions of moksha, nirvana, kevala, or spiritual realization without possessing the experiential state to which these concepts refer. Consequently, linguistic fluency should not be confused with spiritual realization.
This is a central methodological principle for AI-enabled Dharmic Studies: representation is not realization. A model can represent a teaching without embodying its existential significance. The scholarly task is therefore to use AI to investigate philosophical discourse. At the same time, scholars must resist the temptation to treat AI-generated explanations as substitutes for genuine philosophical engagement.
Dharmic Studies provides an especially fertile field for such experimentation (Singler, 2024). Hindu, Buddhist, Jain, and Sikh traditions encompass extensive textual and oral corpora. These corpora extend across Sanskrit, Pali, Prakrit, Ardhamagadhi, Apabhraṃśa, Tamil, Punjabi, Hindi, Bengali, Tibetan, Chinese, Persian, and numerous other languages. Their intellectual histories also encompass multiple schools, commentarial traditions, debates, and translations. They further include manuscript variants, philosophical systems, and diverse regional interpretations. Therefore, conventional scholarship can be constrained by the enormous scale and linguistic diversity of the material.
AI can potentially transform this situation by enabling researchers to search, classify, compare, translate, visualize, and analyze large corpora. Topic modelling, for example, has already been applied to Hindu philosophical literature. Chandra and Ranjan (2022) used BERT-based methods to investigate thematic relationships between the Upanishads and the Bhagavad Gita. Their study demonstrated how computational methods can supplement traditional textual interpretation (Chandra & Ranjan, 2022).
Yet transformation should not be confused with replacement. Religious texts are not merely datasets. Their meanings emerge through language, history, commentary, practice, community, ritual, philosophy, and lived experience. AI therefore presents Dharmic Studies with an extraordinary research opportunity (Smith, 2024). However, it also raises fundamental methodological challenges: How can computational intelligence expand scholarship without reducing Dharmic knowledge to computationally measurable information?
2. Research Methodology
This study employs a qualitative, interdisciplinary, and exploratory research methodology to examine the transformative influence of AI on Dharmic Studies. The research adopts a comparative approach encompassing Hinduism, Buddhism, Jainism, and Sikhism. It pays particular attention to their textual, philosophical, linguistic, ethical, and cultural dimensions. Primary sources include selected canonical and philosophical texts, such as the Vedas and Upanishads, the Bhagavad Gita, Buddhist scriptures, Jain literature, and Sri Guru Granth Sahib. Secondary sources comprise peer-reviewed academic publications, scholarly monographs, digital humanities research, and authoritative reports on AI and education. The study also incorporates conceptual analysis to examine the potential of AI-assisted textual analysis, natural-language processing, and machine translation for the study of Dharmic traditions. It further considers how manuscript digitization, computational comparison, and generative AI may influence this field of study.
A critical literature review method is employed to identify AI-related opportunities. It also examines limitations related to algorithmic bias, hallucination, cultural reductionism, linguistic inequity, and questions of epistemic authority. The analysis is guided by a human-centred and source-critical framework. It emphasizes transparency, contextual interpretation, cultural sensitivity, and scholarly verification. Rather than treating AI as an autonomous interpretive authority, the methodology conceptualizes it as an analytical instrument that can complement humanistic and philological scholarship. However, AI is not regarded as a replacement for human judgment, expertise, or rigorous scholarly inquiry.
3. Conceptual Foundations: Understanding AI within Dharmic Studies
AI should first be understood as a collection of computational techniques rather than as an independent source of knowledge. Machine-learning systems identify patterns from training data. NLP systems process linguistic information. Large language models generate statistically probable sequences of language. Computer-vision systems analyze visual patterns. Moreover, retrieval-augmented systems connect generative models with external databases. These capabilities can be extremely valuable for scholarship. However, they do not automatically constitute understanding in the philosophical or hermeneutical sense (Singler, 2024; Boddington, 2023).
This distinction is particularly important in Dharmic Studies. Concepts such as dharma, karma, moksha, nirvana, ahimsa, seva, hukam, naam, anatman, and tattva carry historically layered meanings. These terms cannot always be translated into equivalent English concepts. A computational system may identify occurrences of a term and establish statistical relationships. However, determining its philosophical significance requires historical and interpretive knowledge.
The challenge becomes more complicated because AI systems inherit patterns from their training data. Research has demonstrated that machine-learning systems can reproduce social and cultural biases embedded in linguistic corpora (Caliskan et al., 2017). Prabhakaran et al. (2022) similarly argue that AI systems can exhibit cultural incongruities because their development and training often reflect limited geographic and cultural contexts. This is particularly significant for Dharmic Studies because much Indic knowledge remains unevenly represented in digitally accessible datasets.
Accordingly, AI should be positioned as a research collaborator and analytical instrument, not an autonomous authority on Dharmic truth. Its strongest contribution may lie in expanding the scale and speed of scholarly investigation. However, interpretation, contextualization, validation, and normative judgment should be left to qualified human scholars and communities.
4. AI and the Transformation of Dharmic Textual Scholarship
One of the most significant applications of AI in Dharmic Studies is computational textual analysis. Traditional scholarship often requires researchers to work painstakingly through manuscripts, editions, translations, and commentaries. It also requires careful engagement with secondary literature. AI can assist by rapidly identifying recurring terms, conceptual associations, and stylistic patterns across large corpora. It can also identify textual parallels and variations.
4.1 Digital Preservation and Manuscript Studies
Many Dharmic traditions possess extensive manuscript collections. The physical preservation of these manuscripts is threatened by deterioration, environmental conditions, limited institutional resources, and restricted accessibility. Digitization provides an important mechanism for preserving these valuable materials. AI-based optical character recognition and handwriting recognition can potentially make digitized manuscripts computationally searchable.
Computer vision can assist in identifying damaged characters and reconstructing probable textual sequences. It can also help classify manuscripts and compare different textual witnesses. Such capabilities could be particularly valuable for Sanskrit, Pali, Prakrit, Gurmukhi, Tibetan, and other scripts. The historical forms of these scripts may present difficulties for conventional OCR systems.
However, computational reconstruction must remain probabilistic rather than authoritative. An AI-generated restoration of a damaged manuscript represents a hypothesis rather than the original text. Scholarly editions must therefore clearly distinguish between manuscript evidence and machine-generated reconstructions.
AI can also facilitate intertextual discovery. A researcher studying a particular philosophical concept could search thousands of texts for linguistic parallels. The researcher could also identify conceptual parallels across different texts. Rather than replacing close reading, this approach would help scholars identify previously unnoticed relationships. These relationships could then be investigated through conventional philological methods.
Such an approach could transform Dharmic textual scholarship (Singh, 2023b-c; Singh, 2024a-b). It could move scholarship beyond predominantly sequential reading. It would instead promote a complementary model that combines close reading and distant reading. The objective would not be to make scholarship less human, but to enable scholars to ask larger and more sophisticated questions.
5. AI, Translation, and the Accessibility of Dharmic Knowledge
Translation represents another transformative area. Dharmic traditions have historically travelled across linguistic boundaries through translation and commentary. AI-based translation systems can accelerate this process. This can be done by making texts available to audiences who lack proficiency in their original languages.
This opportunity is particularly significant because Dharmic literature contains concepts for which direct lexical equivalents may not exist in modern English or other languages. AI can generate preliminary translations and compare multiple translations. It can also identify divergent renderings and explain possible semantic fields. Such tools could therefore function as research assistants for students and scholars.
However, automated translation presents substantial risks. A translation system trained primarily on modern or secondary sources may reproduce conventional interpretations. It may overlook alternative meanings (Doshi, 2024). It may also translate a technical philosophical term according to its most statistically common contemporary meaning rather than its historical usage. In religious studies, such errors can alter the philosophical argument of an entire passage.
For example, translating dharma simply as “religion,” karma as “fate,” or nirvana as “heaven” would create conceptual distortions. Similarly, Sikh concepts such as hukam, haumai, nadar, and naam require contextual interpretation rather than dictionary-level substitution Singh, 2023b-c).
AI translation should therefore follow a human-in-the-loop model. Scholars with linguistic and disciplinary expertise should verify generated translations against primary texts and grammatical structures. They must also confirm historical context and established commentarial traditions. AI can increase accessibility. However, accessibility without accuracy can produce large-scale misinformation.
The goal should consequently not be “AI translation replacing scholars.” Instead, it should be AI-assisted multilingual scholarship. In this approach, computational systems generate possible translations and interpretations, while human experts determine which interpretations are academically defensible.
6. AI and Comparative Study of Hinduism, Buddhism, Jainism, and Sikhism
AI offers particularly promising possibilities for comparative Dharmic Studies (Pandya, 2024; Zheng, 2024). Conventional comparative religion frequently depends upon carefully selected texts and concepts. Computational methods could broaden this process by examining large corpora across traditions. Moreover, these methods can identify similarities, differences, and conceptual networks.
For example, AI could map the occurrence of concepts concerning ethics, liberation, suffering, consciousness, compassion, nonviolence, duty, selfhood, attachment, equality, and transcendence. It could also examine the contextual relationships among these concepts across different philosophical and religious traditions. Such computational mapping could reveal patterns that invite further philosophical investigation.
However, comparative analysis requires methodological caution. Similar vocabulary does not necessarily indicate identical philosophical meaning. Concepts such as karma appear across several Dharmic traditions. However, their precise meanings differ considerably. Likewise, concepts of liberation in Hindu philosophical schools, Buddhism, Jainism, and Sikhism cannot be treated as interchangeable. Although these traditions address human suffering and spiritual transformation, their understandings of liberation differ significantly.
AI, therefore, has the greatest value when it identifies questions rather than prematurely supplying conclusions. A computational model might reveal that two traditions frequently associate certain concepts. However, to determine why those associations occur requires contextual and historical scholarship.
This approach also challenges simplistic narratives of either absolute similarity or absolute difference. AI-supported corpus analysis could help scholars investigate intellectual continuities and historical interactions with greater precision. It could also facilitate the study of borrowings, transformations, and conceptual divergences.
Chandra and Ranjan’s (2022) computational comparison of the Upanishads and Bhagavad Gita illustrates this possibility. Their findings suggest substantial thematic overlap between the corpora. They also demonstrate that computational similarity can serve as a starting point for philosophical inquiry. It should not be regarded as the endpoint of such inquiry. Thus, AI could encourage a more systematic approach to comparative Dharmic scholarship. At the same time, it could help preserve the distinctiveness of individual traditions.
7. AI and the Study of Dharmic Philosophy
The philosophical traditions of Hinduism, Buddhism, Jainism, and Sikhism contain sophisticated discussions of consciousness and knowledge. They also explore causality, ethics, and liberation. In addition, these traditions examine language, perception, selfhood, and reality. AI creates new possibilities for studying these philosophical systems (Chandra & Ranjan, 2022; Bajracharya, 2024; Singler, 2024; Singh, 2023b-c; Singh, 2024c). It can support research through computational modelling and conceptual analysis.
One potential application is philosophical concept mapping. AI can identify relationships among terms across extensive philosophical corpora. It can also visualize these relationships as conceptual networks. Researchers could investigate how particular concepts develop across different texts and schools. They could further examine their evolution across historical periods and commentaries.
AI can also assist in comparing philosophical arguments. A researcher might use computational tools to identify relevant passages. These passages may deal with questions about the nature of the self. They may also examine the relationship between action and consequence. Other passages may address the possibility of liberation. Some may focus on the sources of valid knowledge. Computational systems can help organize this relevant evidence. Such organization can be useful before detailed philosophical analysis begins.
Yet computational representation creates a philosophical danger. One danger is the assumption that what can be quantified is necessarily what matters. Dharmic philosophies frequently distinguish intellectual information from transformative knowledge. Knowledge, in several Dharmic traditions, involves more than possessing propositions. It may also involve a transformation of consciousness, conduct, perception, or attachment.
This distinction has implications for AI itself. An AI system can reproduce descriptions of moksha, nirvana, kevala, or spiritual realization without possessing the experiential state to which these concepts refer. Consequently, linguistic fluency should not be confused with spiritual realization.
This is a central methodological principle for AI-enabled Dharmic Studies: representation is not realization. A model can represent a teaching without embodying its existential significance. The scholarly task is therefore to use AI to investigate philosophical discourse. At the same time, scholars must resist the temptation to treat AI-generated explanations as substitutes for genuine philosophical engagement.
8. AI, Sikh Studies, and the Preservation of Gurbani and Sikh Heritage
AI presents particularly important possibilities and challenges for Sikh Studies (Singh, 2023b-c; Singh, 2024c). The digitization of Gurbani, historical documents, manuscripts, Janamsakhi literature, Rahit literature, and Sikh historiography creates opportunities for sophisticated computational research. Natural Language Processing (NLP) can assist in recognizing the Gurmukhi script. It can also support transliteration and linguistic analysis. Furthermore, NLP can facilitate the creation of concordances and thematic classifications. It may also enhance multilingual accessibility to Sikh textual resources.
AI could help researchers identify recurring concepts within the Guru Granth Sahib. It could also enable researchers to compare the linguistic and semantic contexts in which these concepts appear. AI may further support research into the historical development of Punjabi and Gurmukhi (Singh, 2023a,d). In addition, it could facilitate comparisons among different translations. It could also assist in examining diverse commentarial traditions.
However, Sikh textual scholarship requires exceptional attention to source authenticity. It also requires careful contextual interpretation. The generation of inaccurate Gurbani can have serious consequences. Invented quotations can also mislead readers and researchers. Distorted translations may further contribute to misunderstanding. Fabricated historical narratives can have particularly damaging effects on Sikh education. They can also influence public understanding of Sikh history and tradition. Recent reporting has documented concerns raised by Sikh institutional authorities. These concerns relate to AI-generated misinformation about Sikh teachings, history, Gurbani, and sacred imagery (Singh, 2023c).
The problem, therefore, is not simply technological accuracy. It is also a question of epistemic authority. A fluent AI response may appear credible to users. However, it may lack a reliable primary source. Sikh Studies should consequently prioritize verified textual corpora. It should also emphasize transparent citations and scholarly review. Provenance tracking should be another important requirement.
AI can be highly valuable for Sikh Studies. Its development, however, should involve collaboration among linguists, historians, Sikh scholars, technologists, libraries, and relevant institutions. The creation of authoritative Sikh digital corpora would be particularly beneficial. These corpora should be properly annotated and carefully verified. Such resources could help future AI systems access reliable representations of Sikh textual and intellectual traditions. They could also reduce dependence on fragmented and potentially unreliable online material.
9. Ethical Challenges: Bias, Hallucination, Cultural Reductionism, and Epistemic Authority
The transformative possibilities of AI must be balanced against substantial ethical risks. One of the most important is algorithmic bias. AI systems learn from existing data. Existing data often contain historical inequalities, interpretive biases, translation errors, colonial classifications, and uneven cultural representation (Doshi, 2024). Consequently, an AI system may reproduce dominant interpretations. It may also marginalize traditions that are less well represented digitally.
Hallucination presents a second major problem. Generative AI can produce plausible but unsupported statements. It can also fabricate citations and invent quotations. In some cases, it may create incorrect historical associations. Such errors are particularly concerning in Dharmic Studies, where textual precision is essential. Even seemingly minor inaccuracies can distort the interpretation of religious texts and traditions.
A third concern is cultural reductionism. Dharmic traditions may be reduced to collections of philosophical concepts. These concepts may include karma, yoga, meditation, reincarnation, nonviolence, and mindfulness. Such simplification can obscure the internal diversity of these traditions. It can also diminish their historical and cultural complexity. AI systems trained on simplified popular descriptions may inadvertently reinforce this reductionism.
A fourth issue concerns authority. AI-generated answers may appear neutral or objective to users. This perception can arise because such answers are often expressed with confidence and fluency. However, AI does not possess an independent standpoint outside the systems that produce its responses. Its outputs are shaped by its training data, design choices, retrieval systems, and underlying algorithms. Consequently, explainability is a crucial component of responsible AI scholarship (Arrieta et al., 2020). Accountability is equally important when AI is used in academic and research contexts.
UNESCO (2023) similarly emphasizes the importance of human agency in the use of generative AI. It also highlights the need to protect cultural and linguistic diversity. Transparency and privacy are further identified as important considerations. Ethical safeguards are particularly necessary when generative AI is applied to education and research (Miao & Holmes, 2023). Dharmic Studies should therefore adopt explicit principles of source verification. These principles should also include provenance, contextualization, and transparency. Human oversight must remain central to the interpretation and evaluation of AI-generated material. Cultural sensitivity and epistemic humility are equally important. The most dangerous AI system for religious scholarship is not necessarily one that refuses to answer. Rather, it may be one that answers confidently without providing adequate evidence.
10. Toward a Human-Centred Framework for AI-Enabled Dharmic Studies
A responsible future for AI in Dharmic Studies requires an interdisciplinary framework. This framework should integrate technology with philology, religious studies, philosophy, history, linguistics, anthropology, manuscript studies, and community knowledge. The central principle should be that AI serves scholarship. It should not define scholarship.
First, Dharmic AI systems should be developed from carefully curated primary-source corpora. Whenever possible, texts should be digitized from authoritative editions. These texts should also be accompanied by detailed metadata. Such metadata should identify provenance, language, date, manuscript history, edition, and editorial status.
Second, AI outputs should remain verifiable. A scholarly AI system should provide the textual basis for its conclusions. It should not simply generate unsupported prose. Retrieval-augmented approaches may be especially valuable in this context. Such approaches can link generated responses to specified, identifiable source collections.
Third, multilingual development is essential. Sanskrit, Pali, Prakrit, Punjabi, Gurmukhi, Tamil, Tibetan, and other Indic and Asian languages should not be treated as secondary extensions of English-language systems. Linguistic diversity is fundamental to accurate Dharmic scholarship. AI systems must therefore recognize and respect the linguistic complexity of Dharmic traditions.
Fourth, scholars should be trained in AI literacy. Religious-studies researchers need to understand the limitations of AI models. They should also recognize the possibility of hallucination. They need to be aware of algorithmic bias. Prompt dependence is another important issue to understand. Researchers should examine the provenance of the data used by AI systems. They should also critically evaluate how algorithms interpret religious texts and concepts.
Finally, Dharmic communities should participate in decisions about how sacred materials are digitized. They should also have a voice in how these materials are represented. Their participation should extend to decisions about how sacred materials are disseminated. Religious scholarship cannot be reduced entirely to technological expertise. Collaborative governance can help ensure that computational accessibility does not become cultural appropriation. It can also help prevent epistemic distortion.
Such a framework reflects UNESCO’s broader human-centred approach to generative AI. This approach emphasizes ethical use of AI. It also promotes safe and equitable applications. Meaningful use is prioritized over the adoption of technology for its own sake (Miao & Holmes, 2023).
11. Future Directions and Research Agenda
The next stage of AI-enabled Dharmic Studies should move beyond simple chatbots and automated translation. It should develop toward integrated research ecosystems. Such ecosystems could combine digitized manuscripts, multilingual corpora, knowledge graphs, citation databases, linguistic models, image-recognition systems, and scholarly commentary. These components could work together to support more sophisticated forms of research. They could also enable scholars to examine Dharmic traditions from textual, linguistic, historical, and philosophical perspectives.
One promising direction is the development of Dharmic knowledge graphs. These systems could map relationships among texts, authors, schools, concepts, historical periods, places, philosophical arguments, and commentarial traditions. They could reveal connections that may be difficult to identify through conventional research methods. Researchers could then explore intellectual networks across centuries. This approach would allow them to examine traditions as interconnected systems rather than treating individual texts as isolated objects.
A second direction is AI-assisted manuscript reconstruction and paleography. Computer vision could help classify different scripts. It could also identify distinctive scribal patterns and compare manuscript families. AI systems could further assist in detecting textual variations across different manuscript witnesses. Such technologies could substantially accelerate the cataloguing of manuscripts. They could also strengthen efforts to preserve fragile textual heritage. Together, these developments could create new possibilities for the systematic study, preservation, and interpretation of Dharmic knowledge.
Third, AI could support comparative philosophy by constructing multilingual conceptual maps across Dharmic traditions. Such maps could facilitate systematic comparison among diverse philosophical systems. Researchers could investigate how ideas concerning selfhood, suffering, liberation, ethics, causality, consciousness, and nonviolence are articulated across different traditions. They could also examine the similarities and differences in the ways these concepts are interpreted.
Fourth, AI could transform the teaching of Dharmic philosophy. Students could interact with curated textual databases. They could compare different translations of philosophical and scriptural texts. They could also examine philosophical arguments in greater detail. Furthermore, students could explore the historical contexts in which these ideas developed. Such tools could make complex philosophical traditions more accessible and interactive. Nevertheless, UNESCO stresses that AI should supplement rather than displace human educational relationships and critical thinking (Miao & Holmes, 2023).
Finally, research should investigate AI itself through the lens of Dharmic philosophical categories. Questions concerning consciousness could provide an important framework for examining the nature of artificial intelligence. Questions concerning agency and ethical responsibility could further illuminate the role of humans in the development and deployment of AI systems. Concepts such as attachment, nonviolence, and interdependence could also offer valuable perspectives on AI development. The principle of human flourishing could provide another important framework for evaluating the social consequences of AI. Recent scholarship has begun examining how religious ethical frameworks can contribute to debates on AI governance. This scholarship suggests that religious ethics may have a broader role in shaping technology policy (Tampubolon & Nadeak, 2024).
12. Conclusion
Artificial Intelligence has the potential to become one of the most consequential methodological developments in Dharmic Studies since the emergence of modern print scholarship. Its capacity to process enormous textual corpora can significantly expand the boundaries of scholarship. AI can also identify patterns across large bodies of literature. It can facilitate multilingual research and assist in the preservation of manuscripts. Furthermore, it can support the comparative study of philosophical concepts. AI can also improve access to previously difficult-to-reach scholarly resources. Computational analysis has already demonstrated its potential in the study of Hindu philosophical literature. Broader developments in generative AI are opening new possibilities for education and research
Yet technological capability does not automatically produce scholarly validity. Dharmic traditions cannot be adequately understood through statistical correlations alone. Their texts possess linguistic, historical, philosophical, ritual, ethical, and experiential dimensions that require human interpretation. AI can identify where a concept appears, but it cannot independently determine what that concept means within every historical and philosophical context.
The central challenge is therefore not whether Dharmic Studies should adopt AI. The more important question is how it should adopt AI without surrendering scholarly judgment. The answer lies in developing a human-centred model of AI-assisted scholarship. Such a model should be grounded in primary sources, linguistic diversity, transparent methodologies, and expert verification. It should also recognize the importance of cultural sensitivity and community participation (Prabhakaran, Qadri & Hutchinson, 2022).
AI should function neither as an unquestionable oracle nor as a threat to traditional scholarship. Instead, it should be understood as a powerful intellectual instrument. Properly governed, AI can broaden the range of questions scholars can ask. It can also assist researchers in examining large bodies of textual, linguistic, historical, and cultural material. However, responsibility for interpretation, evaluation, and the determination of meaning must remain with human researchers.
The future of Dharmic Studies may consequently be neither purely traditional nor purely technological. It may instead emerge through a dialogue between ancient wisdom and contemporary computational intelligence. In this dialogue, AI can expand the reach and capabilities of scholarship. At the same time, Dharmic intellectual traditions can provide ethical and philosophical resources for determining how intelligence, whether artificial or human, should serve knowledge, society, and human flourishing.
ReferencesAI could help researchers identify recurring concepts within the Guru Granth Sahib. It could also enable researchers to compare the linguistic and semantic contexts in which these concepts appear. AI may further support research into the historical development of Punjabi and Gurmukhi (Singh, 2023a,d). In addition, it could facilitate comparisons among different translations. It could also assist in examining diverse commentarial traditions.
However, Sikh textual scholarship requires exceptional attention to source authenticity. It also requires careful contextual interpretation. The generation of inaccurate Gurbani can have serious consequences. Invented quotations can also mislead readers and researchers. Distorted translations may further contribute to misunderstanding. Fabricated historical narratives can have particularly damaging effects on Sikh education. They can also influence public understanding of Sikh history and tradition. Recent reporting has documented concerns raised by Sikh institutional authorities. These concerns relate to AI-generated misinformation about Sikh teachings, history, Gurbani, and sacred imagery (Singh, 2023c).
The problem, therefore, is not simply technological accuracy. It is also a question of epistemic authority. A fluent AI response may appear credible to users. However, it may lack a reliable primary source. Sikh Studies should consequently prioritize verified textual corpora. It should also emphasize transparent citations and scholarly review. Provenance tracking should be another important requirement.
AI can be highly valuable for Sikh Studies. Its development, however, should involve collaboration among linguists, historians, Sikh scholars, technologists, libraries, and relevant institutions. The creation of authoritative Sikh digital corpora would be particularly beneficial. These corpora should be properly annotated and carefully verified. Such resources could help future AI systems access reliable representations of Sikh textual and intellectual traditions. They could also reduce dependence on fragmented and potentially unreliable online material.
9. Ethical Challenges: Bias, Hallucination, Cultural Reductionism, and Epistemic Authority
The transformative possibilities of AI must be balanced against substantial ethical risks. One of the most important is algorithmic bias. AI systems learn from existing data. Existing data often contain historical inequalities, interpretive biases, translation errors, colonial classifications, and uneven cultural representation (Doshi, 2024). Consequently, an AI system may reproduce dominant interpretations. It may also marginalize traditions that are less well represented digitally.
Hallucination presents a second major problem. Generative AI can produce plausible but unsupported statements. It can also fabricate citations and invent quotations. In some cases, it may create incorrect historical associations. Such errors are particularly concerning in Dharmic Studies, where textual precision is essential. Even seemingly minor inaccuracies can distort the interpretation of religious texts and traditions.
A third concern is cultural reductionism. Dharmic traditions may be reduced to collections of philosophical concepts. These concepts may include karma, yoga, meditation, reincarnation, nonviolence, and mindfulness. Such simplification can obscure the internal diversity of these traditions. It can also diminish their historical and cultural complexity. AI systems trained on simplified popular descriptions may inadvertently reinforce this reductionism.
A fourth issue concerns authority. AI-generated answers may appear neutral or objective to users. This perception can arise because such answers are often expressed with confidence and fluency. However, AI does not possess an independent standpoint outside the systems that produce its responses. Its outputs are shaped by its training data, design choices, retrieval systems, and underlying algorithms. Consequently, explainability is a crucial component of responsible AI scholarship (Arrieta et al., 2020). Accountability is equally important when AI is used in academic and research contexts.
UNESCO (2023) similarly emphasizes the importance of human agency in the use of generative AI. It also highlights the need to protect cultural and linguistic diversity. Transparency and privacy are further identified as important considerations. Ethical safeguards are particularly necessary when generative AI is applied to education and research (Miao & Holmes, 2023). Dharmic Studies should therefore adopt explicit principles of source verification. These principles should also include provenance, contextualization, and transparency. Human oversight must remain central to the interpretation and evaluation of AI-generated material. Cultural sensitivity and epistemic humility are equally important. The most dangerous AI system for religious scholarship is not necessarily one that refuses to answer. Rather, it may be one that answers confidently without providing adequate evidence.
10. Toward a Human-Centred Framework for AI-Enabled Dharmic Studies
A responsible future for AI in Dharmic Studies requires an interdisciplinary framework. This framework should integrate technology with philology, religious studies, philosophy, history, linguistics, anthropology, manuscript studies, and community knowledge. The central principle should be that AI serves scholarship. It should not define scholarship.
First, Dharmic AI systems should be developed from carefully curated primary-source corpora. Whenever possible, texts should be digitized from authoritative editions. These texts should also be accompanied by detailed metadata. Such metadata should identify provenance, language, date, manuscript history, edition, and editorial status.
Second, AI outputs should remain verifiable. A scholarly AI system should provide the textual basis for its conclusions. It should not simply generate unsupported prose. Retrieval-augmented approaches may be especially valuable in this context. Such approaches can link generated responses to specified, identifiable source collections.
Third, multilingual development is essential. Sanskrit, Pali, Prakrit, Punjabi, Gurmukhi, Tamil, Tibetan, and other Indic and Asian languages should not be treated as secondary extensions of English-language systems. Linguistic diversity is fundamental to accurate Dharmic scholarship. AI systems must therefore recognize and respect the linguistic complexity of Dharmic traditions.
Fourth, scholars should be trained in AI literacy. Religious-studies researchers need to understand the limitations of AI models. They should also recognize the possibility of hallucination. They need to be aware of algorithmic bias. Prompt dependence is another important issue to understand. Researchers should examine the provenance of the data used by AI systems. They should also critically evaluate how algorithms interpret religious texts and concepts.
Finally, Dharmic communities should participate in decisions about how sacred materials are digitized. They should also have a voice in how these materials are represented. Their participation should extend to decisions about how sacred materials are disseminated. Religious scholarship cannot be reduced entirely to technological expertise. Collaborative governance can help ensure that computational accessibility does not become cultural appropriation. It can also help prevent epistemic distortion.
Such a framework reflects UNESCO’s broader human-centred approach to generative AI. This approach emphasizes ethical use of AI. It also promotes safe and equitable applications. Meaningful use is prioritized over the adoption of technology for its own sake (Miao & Holmes, 2023).
11. Future Directions and Research Agenda
The next stage of AI-enabled Dharmic Studies should move beyond simple chatbots and automated translation. It should develop toward integrated research ecosystems. Such ecosystems could combine digitized manuscripts, multilingual corpora, knowledge graphs, citation databases, linguistic models, image-recognition systems, and scholarly commentary. These components could work together to support more sophisticated forms of research. They could also enable scholars to examine Dharmic traditions from textual, linguistic, historical, and philosophical perspectives.
One promising direction is the development of Dharmic knowledge graphs. These systems could map relationships among texts, authors, schools, concepts, historical periods, places, philosophical arguments, and commentarial traditions. They could reveal connections that may be difficult to identify through conventional research methods. Researchers could then explore intellectual networks across centuries. This approach would allow them to examine traditions as interconnected systems rather than treating individual texts as isolated objects.
A second direction is AI-assisted manuscript reconstruction and paleography. Computer vision could help classify different scripts. It could also identify distinctive scribal patterns and compare manuscript families. AI systems could further assist in detecting textual variations across different manuscript witnesses. Such technologies could substantially accelerate the cataloguing of manuscripts. They could also strengthen efforts to preserve fragile textual heritage. Together, these developments could create new possibilities for the systematic study, preservation, and interpretation of Dharmic knowledge.
Third, AI could support comparative philosophy by constructing multilingual conceptual maps across Dharmic traditions. Such maps could facilitate systematic comparison among diverse philosophical systems. Researchers could investigate how ideas concerning selfhood, suffering, liberation, ethics, causality, consciousness, and nonviolence are articulated across different traditions. They could also examine the similarities and differences in the ways these concepts are interpreted.
Fourth, AI could transform the teaching of Dharmic philosophy. Students could interact with curated textual databases. They could compare different translations of philosophical and scriptural texts. They could also examine philosophical arguments in greater detail. Furthermore, students could explore the historical contexts in which these ideas developed. Such tools could make complex philosophical traditions more accessible and interactive. Nevertheless, UNESCO stresses that AI should supplement rather than displace human educational relationships and critical thinking (Miao & Holmes, 2023).
Finally, research should investigate AI itself through the lens of Dharmic philosophical categories. Questions concerning consciousness could provide an important framework for examining the nature of artificial intelligence. Questions concerning agency and ethical responsibility could further illuminate the role of humans in the development and deployment of AI systems. Concepts such as attachment, nonviolence, and interdependence could also offer valuable perspectives on AI development. The principle of human flourishing could provide another important framework for evaluating the social consequences of AI. Recent scholarship has begun examining how religious ethical frameworks can contribute to debates on AI governance. This scholarship suggests that religious ethics may have a broader role in shaping technology policy (Tampubolon & Nadeak, 2024).
12. Conclusion
Artificial Intelligence has the potential to become one of the most consequential methodological developments in Dharmic Studies since the emergence of modern print scholarship. Its capacity to process enormous textual corpora can significantly expand the boundaries of scholarship. AI can also identify patterns across large bodies of literature. It can facilitate multilingual research and assist in the preservation of manuscripts. Furthermore, it can support the comparative study of philosophical concepts. AI can also improve access to previously difficult-to-reach scholarly resources. Computational analysis has already demonstrated its potential in the study of Hindu philosophical literature. Broader developments in generative AI are opening new possibilities for education and research
Yet technological capability does not automatically produce scholarly validity. Dharmic traditions cannot be adequately understood through statistical correlations alone. Their texts possess linguistic, historical, philosophical, ritual, ethical, and experiential dimensions that require human interpretation. AI can identify where a concept appears, but it cannot independently determine what that concept means within every historical and philosophical context.
The central challenge is therefore not whether Dharmic Studies should adopt AI. The more important question is how it should adopt AI without surrendering scholarly judgment. The answer lies in developing a human-centred model of AI-assisted scholarship. Such a model should be grounded in primary sources, linguistic diversity, transparent methodologies, and expert verification. It should also recognize the importance of cultural sensitivity and community participation (Prabhakaran, Qadri & Hutchinson, 2022).
AI should function neither as an unquestionable oracle nor as a threat to traditional scholarship. Instead, it should be understood as a powerful intellectual instrument. Properly governed, AI can broaden the range of questions scholars can ask. It can also assist researchers in examining large bodies of textual, linguistic, historical, and cultural material. However, responsibility for interpretation, evaluation, and the determination of meaning must remain with human researchers.
The future of Dharmic Studies may consequently be neither purely traditional nor purely technological. It may instead emerge through a dialogue between ancient wisdom and contemporary computational intelligence. In this dialogue, AI can expand the reach and capabilities of scholarship. At the same time, Dharmic intellectual traditions can provide ethical and philosophical resources for determining how intelligence, whether artificial or human, should serve knowledge, society, and human flourishing.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. Redirecting
Bajracharya, R. (2024, May). AI and Buddhism. Lumbini. 9-11. https://www.academia.edu/120157613/AI_and_Buddhism
Boddington, P. (2023). AI ethics: a textbook. Singapore: Springer.
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://doi.org/10.1126/science.aal4230
Chandra, R., & Ranjan, M. (2022). Artificial intelligence for topic modelling in Hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita. arXiv. 2205.11020v1. Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita (arXiv)
Doshi, A. (2024). The loss in AI translation. Research Archive of Rising Scholars.
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. UNESCO Guidance for Generative AI in Education and Research
Pandya, C. (2024, May 20). A Hindu Perspective on AI Risks and Opportunities. Future of Life Institute. futureoflife.org A Hindu Perspective on AI Risks and Opportunities - Future of Life Institute
Prabhakaran, V., Qadri, R., & Hutchinson, B. (2022). Cultural incongruencies in artificial intelligence. arXiv:2211.13069v1. Cultural Incongruencies in Artificial Intelligence (arXiv)
Singh, D. P. (2023a). Artificial Intelligence and Its Impact on Punjabi Culture. Punjab Dey Rang, Lahore, PK. 17 (3). 5–10.
Singh, D. P. (2023b). Sikhism and Artificial Intelligence - The Mutual Relevance. Journal of Studies in Sikhism and Comparative Religion 49 (2). 34-42.
Singh, D. P. (2023c). Using Artificial Intelligence for Promoting Sikhism- Beneficial or Harmful. Sikh Philosophy Network. Chandigarh. India. Opinion - Using Artificial Intelligence for Promoting Sikhism- Beneficial or Harmful
Singh, D. P. (2023d, Nov.). Artificial Intelligence and Punjabi Culture. International Culture and Art (ICA). Lahore, PK. 5 (4). 11-14.
Singh, D. P. (2024a). Artificial Intelligence’s Impact on Science-Religion Dialogue. The Sikh Bulletin, USA 26 (1). 15-20.
Singh, D.P. (2024b). AI and Sikhism: Ethical Stewardship and Moral Challenges in the Digital Age. SikhNet. sikhnet.com. AI and Sikhism:
Singh, D.P. (2024c). Harnessing Artificial Intelligence for Sikhism: Opportunities and Risks. Understanding Sikhism: The Research Journal, 26(1). 25-34 (2024)
Singler, B. (2024). Religion and artificial intelligence: An introduction (p. 228). Taylor & Francis.
Smith, J. (2024). Faith, Technology, and the Ethics of AI. In Handbook on the Ethics of Artificial Intelligence (pp. 37-48). Edward Elgar Publishing.
Tampubolon, M., & Nadeak, B. (2024). Artificial intelligence and understanding of religion: A moral perspective. International Journal of Multicultural and Multireligious Understanding, 11(8), 903-914.
Zheng, Y. (2024). Buddhist Transformation in the Digital Age: AI (Artificial Intelligence) and Humanistic Buddhism. Religions, 15(1), 79. https://doi.org/10.3390/rel15010079
******
Cit. : Singh, D. P. (2024). “Exploring the Transformative Influence of Artificial Intelligence on Dharmic Studies," Proceedings of the Conference on "AI in Health-Religion/Spirituality-Humanities, " held at the University of Cincinnati, Cincinnati, Ohio, USA, on 23-25th August 2024.
Last edited:
