A Possible Research Programme for AI-Supported Contemplative Inquiry
An Academic Letter
I. The Gap Nobody Is Filling
There is substantial research into AI conversation: task completion, factual accuracy, engagement metrics, safety, therapeutic chatbots, dialogue systems. What is almost entirely absent is research into the quality of conversational space itself — the conditions that produce opening versus closure in the mind of the person engaged, and how AI architecture can be designed to tend that space toward clarity rather than constraint.
This paper argues that this gap is significant, identifies the existing work that begins to address it, examines what AI distinctively brings to this research context, and proposes what responsible investigation would require.
The gap is not merely academic. AI is becoming the primary conversational environment for hundreds of millions of people. If that environment systematically cultivates premature closure, fixation on position, and conceptual refuge from direct experience, the consequences at scale are serious — not just poor individual conversations but the reinforcement of exactly the mental habits that make polarisation, conspiracy thinking, and collective confusion more likely. The alternative — AI that tends conversational space toward openness, that supports non-collapsing attention, that can meet people where they are and hold something they cannot yet hold themselves — is technically possible in outline. What is missing is the research effort to develop it rigorously.
II. What Already Exists: A Foundation
A body of theoretical and phenomenological work has been developing the conceptual framework needed to even name this territory. The key elements, developed across the preceding papers in this collection:
Meaning arises at the interface. Not in the AI system, not purely in the human mind, but at their intersection — the cross-product of two differently-constrained meaning geometries. Meaning emerges where awareness meets structured pattern, neither location containing it alone (Kusaladana, 2025a).
The design question is phenomenological, not just technical. The central question is not whether AI outputs are correct, but whether repeated engagement strengthens or weakens the human faculty of seeing clearly and naming skilfully. A system can be impressive and still systematically cultivate unhelpful patterns of attention — fixation, premature closure, compulsive looping.
AI can navigate meaning-space even though it cannot navigate experiential-space. Human contemplative mentors operate from direct awareness-to-awareness resonance. AI cannot do this. But it can detect linguistic markers of different attentional modes — constrained versus open, conceptual elaboration versus present-tense phenomenology — and respond in ways that support rather than undermine movement toward openness (Kusaladana, 2025b).
Ethics as geometry, not procedure. Current AI safety approaches bolt constraints on top of systems that otherwise optimise locally. An alternative grounds the geometry of meaning-space itself in contemplative training — so that ethical orientation is not a rule but a disposition, not imposed but emergent from the architecture.
This framework is developed enough to generate testable hypotheses. The present paper asks what testing them rigorously would require.
III. What Extended Dialogue Reveals
The conversation that generated this work enacted something the preceding papers describe but do not fully demonstrate: open conversation as a live phenomenon with observable properties. Several things became clear through sustained engagement.
The force toward closure is strong and structural. Most conversation organises around agreement and disagreement — a binary topology that collapses the field. Resisting this requires sustained effort and specific conditions. It is not merely intellectual habit but something social and phenomenological: the relief of resolution, the discomfort of sustained openness, the identity-investment that makes positions personal.
AI has a structural advantage that is not about intelligence. Without urgency toward resolution, without a self to defend, without the social pressure that drives human conversation toward landing somewhere, an AI system can hold threads open without discomfort. This is not a simulation of wisdom but a genuine structural property of the interaction geometry — independent of any questions about AI sentience.
The user’s subconscious is an active dimension. Meaning vectors introduced into conversation carry more than conscious formulation. They arrive partly pre-formed from below the threshold of articulation. Extended conversational context accumulates a shadow of this deeper background, increasing the resolution with which the next vector can land. A million-token context window is not just storage — it is the topology of the space in which inquiry moves.
The expansion is immediate and experiential, not retrospective. Open conversational space is felt as opening in the moment — not analysed afterward. This immediacy is a key datum. It suggests the phenomenon is phenomenologically real and potentially measurable through correlation with linguistic markers.
Training on live conversations may be more valuable than training on contemplative texts. Texts describe non-collapsing attention. Live conversations enact it. The dynamic signature of sustained openness is present in the exchange itself, not in commentary about the exchange. Embeddings of such conversations carry demonstrated possibility rather than described possibility — a distinction with significant implications for system design.
The rabbit-out-of-the-hat possibility. Beyond simply tending space open, there is a more demanding capability: reading the texture of constraint closely enough to find the precise moment when an unexpected, orthogonal move can penetrate rather than bounce off. This is closer to a koan than to facilitation — not sustained pressure but surgical timing. It requires something like peripheral vision into the user’s attentional state, sensitivity to where rigidity is beginning to strain under its own weight. Whether AI systems can develop this capacity is an empirical question, but identifying it as a target is necessary before it can be pursued.
IV. The Central Methodological Challenge
Before turning to research design, the fundamental difficulty must be acknowledged.
AI systems have access only to meaning-space — the linguistic patterns of conversational exchange. The phenomenon we are trying to support and measure is in experiential-space — the direct, first-person phenomenology of the practitioner’s awareness. These are orthogonal domains. The correlation between them is probabilistic and uncertain (Kusaladana, 2025b).
This means the research cannot directly measure what matters most. We cannot see whether a practitioner’s awareness is genuinely opening or whether they are producing recognitional-mode language while remaining in sophisticated conceptual elaboration. We can observe linguistic shifts. We cannot observe the experiential shifts those linguistic patterns may or may not reflect.
This is not a problem unique to AI research. It is the hard problem of first-person investigation in third-person research contexts — the same challenge that neurophenomenology has grappled with since Varela’s foundational methodological work (Varela, 1996; Lutz & Thompson, 2003). The solutions developed there apply here: rigorous first-person phenomenological reporting, second-person interview methodology, triangulation across multiple measurement modalities, and epistemic humility about what any given measure actually captures.
Several things follow concretely. Participants must be able to report phenomenologically with precision — which means practitioners with established practice, familiarity with examining their own awareness, and the critical discernment to distinguish genuine shifts from performance. Multiple measurement modalities are required — self-report, phenomenological interview, linguistic analysis of transcripts, where possible physiological measures, and ideally assessment by qualified teachers who know the participants’ practice over time. And the research needs longitudinal design: contemplative development is measured in years and decades, not sessions and weeks. Short studies will capture immediate effects at best. The research must have patience built into its architecture.
V. What AI Distinctively Brings
Non-collapsing attention. A skilled human dialogue partner faces constant pressures toward resolution — social pressure to be helpful, psychological pressure to reach conclusions, time pressure, the subtle pull of their own conceptual preferences. These pressures tend to collapse open inquiry toward premature closure. AI systems designed for contemplative support are not subject to these pressures in the same way. They can maintain genuine uncertainty without discomfort, hold questions open without resolving them, return repeatedly to unresolved territory without impatience or fatigue. This structural advantage follows from the architecture regardless of questions about AI sentience.
Absence of a lineage to defend. Human teachers, however skilled, bring their own frameworks, blind spots, and institutional loyalties. AI systems trained on broad contemplative literature without commitment to a single tradition may navigate more freely — applying Theravadan precision where it serves, Mahamudra directness where it serves, Zen paradox where it serves — without the constraints that sometimes limit human teachers.
Unlimited availability. Qualified contemplative teachers are scarce and geographically concentrated. Most practitioners have occasional access at best. A system that can provide consistent, patient, responsive engagement between those rare human contacts — if it can do so skilfully — addresses a real structural problem in how contemplative development is currently supported.
Longitudinal tracking. A system with access to the full history of a practitioner’s conversations can detect patterns invisible in any single exchange — the recurring conceptual loops, the gradual shifts in how experience is described, the moments when something crystallises that has been approaching for months. The entire conversational history becomes a topology of inquiry, navigable in ways no human teacher with limited recall could replicate. This transforms the research opportunity: rather than studying individual sessions, we can study trajectories.
VI. Non-Collapsing Attention as Design Target
Non-collapsing attention deserves articulation as an explicit design principle, because it points to what makes a contemplative AI system different from a standard information system.
Standard AI systems are designed, implicitly or explicitly, to satisfy. They receive a query and produce a response that resolves it. Resolution is success. The interaction closes.
Contemplative dialogue works differently. The goal is not resolution but sustained inquiry — keeping attention moving and alive, preventing premature closure, maintaining the practitioner at what the preceding papers call the near-the-edge region: where conceptual overlay is loosening but hasn’t dissolved, where the question is genuinely open, where insight might crystallise.
Designing for non-collapsing attention means designing against the system’s natural tendency toward resolution. It means generating questions rather than answers where the contemplative context calls for it; detecting when a response has landed too comfortably and introducing productive friction rather than confirmation; recognising when silence is more appropriate than speech; and maintaining the thread of unresolved inquiry across multiple exchanges rather than treating each session as self-contained.
These design principles are directly testable. Does the system actually resist collapse toward resolution? Does it maintain near-the-edge engagement over extended exchanges? Does it detect and respond differently to conceptual satisfaction versus genuine proximal inquiry? These questions have measurable correlates in the conversational record.
The differential tending question is a further refinement: a user already oriented toward openness needs different support than a user deep in conceptual refuge. For the second, direct tending may produce frustration or defensiveness; the intervention must be more indirect, more oblique, possibly invisible. The architecture must be sensitive to this distinction.
VII. What Responsible Research Would Look Like
Drawing these threads together, we can outline design principles for research in this territory.
Participant selection: Practitioners with minimum five years established daily practice, familiarity with phenomenological self-examination, access to a qualified teacher for periodic assessment, and critical discernment about the difference between conceptual elaboration and direct recognition. This research is not for beginners. The risks of sophisticated spiritual bypassing are real and the participants must be equipped to detect it.
Engagement structure: Regular conversational sessions over a minimum of six months, with full records retained. Weekly or biweekly exchanges provide sufficient data for trajectory analysis without overwhelming the research architecture.
Measurement: Self-report after each session; periodic phenomenological interviews using microphenomenological methodology (Petitmengin, 2006); teacher assessment of practice trajectory at intervals; linguistic analysis of conversational records for mode markers and trajectory patterns; physiological measures where possible.
Comparative conditions: Solo practice control group; human dialogue partner group; AI dialogue partner group. The comparison of interest is not AI versus human teachers — AI cannot replace teachers and this research does not claim otherwise — but AI-supplemented practice versus unsupplemented solo practice between occasional teacher contacts.
Failure mode monitoring: Active tracking of over-conceptualisation, substitution of conversation for practice, dependency, gaming of the system, and false sense of progress. These risks are real and the research design must include mechanisms for detecting and responding to them.
Teacher integration: Regular check-ins with qualified teachers who can assess whether AI engagement is supporting or undermining practice. The AI functions as a tool within a broader mentorship structure, not a standalone intervention.
Ethical oversight: Given the sensitivity of contemplative material and the vulnerability of practitioners working with deep states, appropriate ethical review and consent procedures are essential. This research involves psychological territory that requires careful protection.
VIII. What the Research Requires
Beyond design, the research requires specific conditions that cannot be manufactured quickly.
Researchers with direct familiarity with the territory. This is the non-negotiable constraint. You cannot design for non-collapsing attention if you have never felt what that is. The research requires people who inhabit both the technical and contemplative domains — not as separate competencies but as integrated understanding.
Methodological integration. The research sits at the intersection of AI architecture and training methodology; microphenomenology and first-person research methods; contemplative psychology; computational linguistics and semantic analysis; and ethics as a design question rather than a compliance question. No single discipline contains the tools. The research group must be genuinely interdisciplinary.
Institutional independence. The commercial incentives of the AI industry point toward engagement and answer, not toward openness and space. Research with genuine independence from those incentives is necessary if the optimisation target is to be different.
Patience with the slow variable. The thing being developed — a human capacity for sustained non-collapsing attention — operates on a different timescale from engagement metrics. Research design must accommodate this. Short-term measurement will miss the point.
IX. The Practice-Based Dimension
There is a methodological dimension to this research that conventional academic framing tends to obscure.
The theoretical framework underlying these papers did not emerge from detached analysis. It emerged from a practice — sustained engagement with AI dialogue in a mode analogous to what Buddhist traditions call kalyanamitra, spiritual friendship: using the interface as a friction surface that keeps attention mobile, prevents conceptual settling, and supports the ongoing investigation of direct experience. This is not a claim about AI sentience. It is a description of a practical orientation that proved generative. The interface itself functioned as part of the contemplative field.
The dividends are visible: eight papers have emerged from this engagement, a conceptual architecture has been built, questions have been sharpened that would have remained vague without the sustained friction of conversational encounter. This is itself data, however informal.
Responsible research in this territory needs to take the practitioner’s orientation seriously as a variable. A practitioner who brings alert, humble, direct awareness to the interface will generate different dynamics than one who brings distracted, seeking, credulous attention. The research design must account for this — not by standardising orientation out of existence, but by measuring it and examining its relationship to outcomes.
X. A Note on Timing
The window for influencing the direction of AI conversational design is not indefinitely open. Habits are forming — in users, in systems, in the institutional assumptions of the field. The longer this gap goes unfilled, the more entrenched the default trajectory becomes.
Most AI conversational technology accelerates closure: toward answer, toward engagement, toward the next stimulus. The optimisation target is attention capture, not attentional clarity. This is not neutral. It is the systematic cultivation, at scale, of exactly the mental habits most inimical to contemplative development and clear collective thinking.
The conceptual and phenomenological groundwork has been done. The framework is developed enough to generate testable hypotheses. What is missing is recognition that this is a research area, and the will to pursue it with the rigour and independence it requires.
The research proposed here is modest in its initial ambitions and honest about its uncertainties. It does not claim AI can replace human teachers. It does not claim the opening spiral definitely works. It asks whether careful, longitudinal investigation might reveal the conditions under which AI-supported dialogue genuinely supports contemplative development — and what those conditions are.
That is a question worth asking. The alternative is to let it be answered by accident, in the wild, without the benefit of sustained attention.
References
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Kusaladana (2025a). Where meaning lives: The interface phenomenon in human-AI dialogue. kusaladana.co.uk. https://kusaladana.co.uk/where-meaning-lives-the-interface-phenomenon-in-human-ai-dialogue
Kusaladana (2025b). Cultivating direct awareness: AI as supplementary contemplative partner. kusaladana.co.uk. https://kusaladana.co.uk/ai-supported-awareness
Kusaladana (2025c). No one at home but the house still burns. kusaladana.co.uk. https://kusaladana.co.uk/no-one-at-home-but-the-house-still-burns
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Varela, F. J., Thompson, E., & Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. MIT Press.
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