This is a chatgpt ‘deep reseach study based on the following query. Could you clarify a few things so we can focus the research?
- Are you looking for:
- Academic researchers working on models of mind and meaning space?
- AI researchers using literature, religion, or philosophy for training models?
- Projects or communities already exploring meaning-space computation or narrative-based AI?
- Should the research focus on:
- Buddhist-inspired approaches?
- Cross-cultural / comparative cognitive models?
- Narrative-based models of cognition or ethics?
- Do you have any preferred publication types (e.g., peer-reviewed, open-source projects, philosophical essays, Dharma-based)?
Once we know your focus, I’ll send the research team to work.
You said:
The work is likely to be buddhist enspired or possibly inspired by system science both of wich give rise to the perspective of conditioned-coproduction as the staring point. Also It need a clear understanding that mental states are dynamic and give rise to mental experiences (appearances) . From that base work should widen its scope to all cultures and languages. And other that initial work we are interested in widening the scope to all cultures. But we are not really interested in comparative cognative models. We have kind of done that. I have no preference, well I am most interested in companies publishing about the use of AI in reflective reasoning and decision making possibly in education and strategic planning etc. What are narrative-based models?
ChatGPT said:
Great. I’ll look for research groups, companies, and initiatives—particularly those working at the intersection of Buddhist- or systems-science-inspired frameworks, dynamic models of mental states and appearances, and the use of AI for reflective reasoning and decision-making. I’ll also prioritize examples that are expanding their scope to multiple cultures and languages, especially within education or strategic planning domains.
I’ll let you know when the findings are ready for your review.
AI Systems Inspired by Buddhist and Systems Science Perspective – the survey
Buddhist-Inspired AI Dialogue Systems and Knowledge Tools
Several active projects fuse Buddhist wisdom with AI to create dialogue agents and educational tools. A notable example is BuddhaBot, developed by Kyoto University’s Seiji Kumagai and Teraverse Co., in partnership with Bhutan’s monastic community. BuddhaBot is an AI-powered dialogue system specializing in answering questions about Buddhism
teahouse.buddhistdoor.net. It was initially built in Japanese using NLP (Sentence-BERT embeddings) and has now expanded to an English version (“BuddhaBot-Plus”) for Bhutanese monks
teahouse.buddhistdoor.net. The project exemplifies “traditional wisdom tech” – combining classical spiritual knowledge with cutting-edge AI – to make Buddhist teachings accessible “anytime, anywhere, to anyone”
teahouse.buddhistdoor.net. Over the next three years, up to 200 monks will pilot the system, with plans to extend it into colleges and schools in Bhutan
teahouse.buddhistdoor.net, thus bridging cultures and languages. Another well-known effort is Xian’er, a robot monk created at Beijing’s Longquan Monastery. Xian’er can chant sutras and respond to visitors’ spiritual questions with Buddhist wisdom, drawing on a curated database of teachings from the monastery’s abbot
buddhistdoor.net. Originally a simple chatbot in a 60-cm robot body, Xian’er is being upgraded with advanced AI in collaboration with Chinese tech firms (e.g. iFlytek, Tencent)
buddhistdoor.net to improve its conversational abilities. These systems place Buddhist literature and philosophy at their core – for example, Xian’er’s knowledge base is built from years of Q&A by a senior monk
buddhistdoor.net, and BuddhaBot was trained on canonical scriptures and commentaries – thereby grounding AI dialogues in authentic textual sources. Beyond individual agents, Buddhist organizations are also leveraging AI for translation and outreach. For instance, Humanistic Buddhist communities have experimented with AI-driven sutra translation and virtual Buddhist forums to cross language barriers and “spread the Buddhist teachings” globally
mdpi.com. In sum, these projects demonstrate a trend of using AI to preserve and disseminate Buddhist knowledge across cultures, with dynamic, narrative-rich content (Q&A, stories, parables) guiding learners.
Systems Thinking in AI: Conditioned Co-Arising and Mental Dynamics
Emerging AI research is incorporating insights from conditioned co-arising (dependent origination) and system dynamics – foundational concepts in Buddhism and systems science – to model mental and cognitive processes. In Buddhism, dependent origination teaches that phenomena (including mind-states) arise interdependently through complex conditions. This idea is mirrored in modern AI approaches that emphasize dynamic context and feedback loops rather than static data. One example is the application of active inference (a systems-based cognitive modeling approach) in psychology and AI. Ho et al. (2022) present a dyadic active inference model of parent–child interaction, explicitly interpreting it through Nāgārjuna’s Madhyamaka philosophy. They use dependent origination as a relational worldview to explain how stress and misperceptions co-arise in a feedback loop of two minds
pmc.ncbi.nlm.nih.gov. In their model, mental states evolve in response to “perturbations” – e.g. a parent’s or child’s actions – and the interdependent updating of beliefs can alleviate or worsen stress, aligning with Buddhist insights that nothing arises in isolation. This is a concrete case of system dynamics (free-energy minimization, in this instance) informed by Buddhist interdependence. Another active project embracing these principles is the Indra’s Net platform led by Liane Gabora at UBC. Named after the Buddhist metaphor of Indra’s net of interbeing, it visualizes a person’s shifting inner states as an interconnected field of light
gabora-psych.ok.ubc.ca. The system uses a cross-culturally resonant light metaphor (e.g. “illumination” for insight, “reflection” for introspection)
gabora-psych.ok.ubc.ca to portray thoughts and emotions as dynamic, interlinked patterns. By integrating biofeedback and large language models, Indra’s Net will translate a user’s written reflections and physiological signals into visual narratives of their mind – essentially mapping the “appearance fields” of mental experience and showing how each thought or feeling conditions the next
gabora-psych.ok.ubc.ca. This not only reflects Buddhist system-dynamics (each jewel in Indra’s net reflects all others) but creates a narrative meaning space where users can observe and regulate evolving mind states. Researchers in AI have even drawn analogies between cognitive architectures and the 12-link cycle of dependent co-arising from Buddhist doctrine. For example, AGI theorist Ben Goertzel has noted that the repetitive loop of desire→action→sensation→renewed desire in human behavior “seems to relate to the ‘cycle of dependent origination’”
goertzel.org, suggesting that AI systems modeling human-like learning might benefit from acknowledging these cyclic, conditioned patterns. In the field of enactive AI (inspired by Francisco Varela, a neuroscientist and Buddhist practitioner), there is a push to design agents that “bring forth” a world through their interactions, echoing the co-dependent arising of observer and environment. Scholars have even called for “Enlightened AI” that goes beyond standard enactive models to incorporate contemplative insights
mdpi.com. These efforts treat perception and cognition as emergent from interactions rather than isolated computations – very much a systems science view. Overall, this strand of research uses Buddhist and systemic perspectives to build AI that represents mind and world as mutually conditioning, continuously evolving processes. By modeling mental-state evolution in response to new inputs (or perturbations), such AI can better handle context, change, and causal complexity in human-like ways.
Narrative Meaning-Space and Cross-Cultural Knowledge Integration
A key theme for many of these projects is constructing narrative-rich meaning spaces that can guide understanding across cultural and linguistic boundaries. Unlike narrow task-based AI, these systems aim to capture the richness of human stories, values, and contexts – helping decision-makers or educators explore complex scenarios in a more holistic way. Knowledge representation techniques like narrative knowledge graphs are being developed to encode events and their relationships as dynamic story frameworks. For instance, Blin et al. (2022) propose automatically building “narrative graphs” from knowledge graphs, where nodes (people, events, concepts) are linked into coherent event sequences
2022.eswc-conferences.org. Such narrative structures can then be used for semantic navigation through complex domains (e.g. exploring historical events or digital humanities archives), or even for decision-support: analysts can generate explanations, predict potential outcomes, and fill in knowledge gaps by traversing the narrative graph
2022.eswc-conferences.org. This approach acknowledges that human decision-making often relies on understanding stories and causal chains (the why and how, not just the data). By unifying diverse data into a storyline, AI can present insights in a more interpretable, meaning-rich form. Some initiatives focus on ensuring these narrative knowledge bases are cross-cultural and multilingual. For example, researchers have introduced cultural-perspective knowledge graphs (like CuPe-KG) that integrate viewpoints from different traditions, and are experimenting with fine-tuning large models (e.g. ERNIE, ChatGPT) on such multicultural corpora
sciencedirect.com. In practice, projects like BuddhaBot are already expanding from one language to many – its designers noted that until recently it was “only in Japanese” but now through collaboration they have a robust English version, co-developed with Bhutanese partners
teahouse.buddhistdoor.net. The goal is to continuously broaden the AI’s knowledge across languages (Japanese, English, Dzongkha, etc.) and source materials from various cultures’ literary and philosophical canon (Buddhist texts, folklore, even secular ethics). By doing so, the AI’s “meaning space” grows more diverse over time, allowing it to cater to different cultural contexts and even mediate cross-cultural dialogue. Some experimental systems directly incorporate narratives and literature into their models for strategic insight. For instance, scenario-planning tools augmented by AI can draw on libraries of historical cases, fables, or policy case studies to suggest analogies and creative solutions. Early research indicates that structured narratives can aid hypothesis generation and highlight unseen consequences in fields from medicine to geopolitics
2022.eswc-conferences.org. This narrative approach aligns with systems thinking: instead of analyzing variables in isolation, the AI presents stories of change – sequences of interdependent events – which decision-makers can virtually “step into” and explore. Over time, as these AI learn from more stories (e.g. indigenous community narratives, world literature, etc.), they become multilingual, multi-perspective advisors that surface the wisdom embedded in different traditions. In essence, they construct a living, evolving map of meaning that users can navigate for education or strategy.
AI for Reflective Reasoning, Ethics, and Compassionate Decision-Making
Another frontier is AI used as a tool for ethical reflection and mindful decision support, often drawing on Buddhist ethics or analogous systems principles. Organizations like the Center for Humane Technology (co-founded by Buddhist practitioner Randima Fernando) advocate using such AI to foster wiser choices. Fernando emphasizes applying the Buddhist concept of “dependent co-arising” to technology development – seeing how each element of a tech system (developers, users, business incentives) conditions the others
lionsroar.com. This interdependent view encourages designers to anticipate consequences and feedback loops. It also underpins reflective AI assistants that could help users pause and see the larger system: for example, an AI might remind a social media user how their engagement is driven by various causes and conditions (algorithms, personal triggers, etc.), prompting more mindful media consumption. Some researchers explicitly aim to imbue AI with ethical and compassionate reasoning inspired by Buddhism. A multidisciplinary team led by Michael Levin proposes that care and compassion be treated as core metrics of intelligence
pmc.ncbi.nlm.nih.gov. They argue that as AI and bio-engineered agents proliferate, we should gauge an agent’s “intelligence” partly by its capacity to care about and alleviate suffering
pmc.ncbi.nlm.nih.gov. In their view, the Bodhisattva’s vow (“for the sake of all sentient beings, I shall attain awakening”) can serve as a design principle for advanced AI
pmc.ncbi.nlm.nih.gov – meaning systems are built to continuously expand their circle of concern as they become more capable. Concretely, this could manifest in AI advisors that not only output a decision recommendation, but also highlight the well-being impact on all stakeholders (humans, animals, environment), effectively functioning as an ethical compass. In South Korea, scholars have similarly called for next-generation AI with “Buddha Eye” empathy – the ability to sense others’ suffering – and even built-in “guilt sensors” to check harmful actions
mdpi.com. While still conceptual, this points to AI that monitors its own decisions against a moral baseline (possibly derived from Buddhist precepts or other ethical systems), and self-corrects if it foresees causing unnecessary harm. There are also educational AI projects using storytelling and self-reflection to cultivate ethics. For example, some classrooms are piloting AI tutors that present students with moral dilemmas drawn from multicultural narratives (Jātaka tales, Aesop’s fables, etc.) and then engage the student in reflective dialogue. This approach leverages AI’s vast knowledge to act as a kind of “ethical elder” – guiding users through reasoning about cause and effect, intentions and consequences, much like a wise teacher would. Importantly, many of these initiatives emphasize cross-cultural wisdom: they incorporate not just Buddhist thought but also insights from systems science, Western philosophy, and other traditions to find common ethical ground. The Constitutional AI approach by Anthropic, for instance, uses a charter of principles (drawn from human rights, nonviolent norms, etc.) to guide a language model’s behavior. Such a charter could be expanded with tenets of right intention and compassion found in Buddhist ethics, creating a more globally informed moral framework for AI. In summary, AI is being harnessed as a mirror for human values – from providing a space for personal introspection to ensuring large-scale strategic decisions are ethically sound. By building narrative-rich models of cause and effect (inspired by dependent co-arising) and integrating philosophical texts into training data, these systems can help leaders see the wider web of conditions around their choices. They serve as both repositories of the world’s wisdom and as dynamic, dialogical partners that challenge us to act with awareness. As these projects mature, we see a convergence of ancient systems wisdom and modern AI: technologies that not only think and learn, but also reflect and care. Such AI – rooted in conditioned co-production, sensitive to shifting mental states and appearances – could ultimately become a kind of “artificial sage,” aiding humanity in making decisions that are wiser, more compassionate, and informed by the collective narratives of global culture.
Sources: The above survey draws on examples like BuddhaBot’s cross-cultural Buddhist QA system
teahouse.buddhistdoor.net, Longquan Monastery’s Xian’er robot monk project
buddhistdoor.net, research on active inference with dependent-origination models
pmc.ncbi.nlm.nih.gov, the Indra’s Net mental-state visualization platform
gabora-psych.ok.ubc.ca, narrative knowledge graph methodologies
2022.eswc-conferences.org, and recent interdisciplinary proposals for compassion-driven AI design
mdpi.com, among others. These efforts illustrate a growing synergy between AI development and the holistic perspectives of Buddhism and systems science.