The investigation rested on semi-structured interviews with 131 academics — 73 men and 58 women — drawn from five universities and colleges across Iran and Canada, two national contexts chosen deliberately for their contrasting governance structures, language ecologies, and levels of technological integration. Purposive sampling targeted faculty who had personally used AI in research writing within the previous twelve months, and candidates without that hands-on experience were excluded. Interviews lasted roughly twenty to thirty minutes and were conducted over Zoom or in person, primarily in English, with a Farsi subset translated and back-checked for conceptual equivalence. Recordings were transcribed with the help of Otter.ai and verified manually, then analyzed through Braun and Clarke’s six-phase reflexive thematic analysis, a qualitative technique that moves from systematic coding through theme development, review, definition, and reporting. Two analysts independently double-coded twenty percent of the transcripts, stratified by discipline and career stage, reaching 85 percent agreement before discrepancies were resolved through discussion, and sampling continued until thematic saturation was achieved.

The dominant first impression was one of efficiency. Faculty described deploying AI for literature reviews, data analysis, manuscript organization, and early drafting, with one social scientist marveling that the technology structures reviews “in minutes, something that used to take weeks.” Well over half reported that AI was especially useful for grammar, language accuracy, and the overall clarity of academic prose. But participants described something subtler than speed. Shahini’s analysis suggests AI is redistributing cognitive labor across the research pipeline: the locating, organizing, and synthesizing that once consumed a scholar’s intellectual resources are increasingly automated, which pushes distinctly human contribution toward judgment, contextual reasoning, and disciplinary understanding. Participants implicitly separated information processing, which machines perform well, from knowledge construction, which they regarded as irreducibly human. In that reading, AI does not diminish scholarly expertise but relocates it — provided researchers remain actively engaged rather than drifting into the role of passive evaluators of machine output, a transition many feared would erode the analytical habits underpinning rigorous science.

The enthusiasm collided hard with the technology’s failure modes. An applied linguistics participant captured the paradox in two sentences: AI “helps me refine my writing,” the participant acknowledged, “but when it generates references, they are often made up.” Roughly half of the faculty reported encountering fabricated or inaccurate citations, a hallmark of large language models, which generate statistically plausible text without querying any verified database of real sources. Participants also flagged factual inaccuracies and superficial analyses, noting that fluent, convincing prose often conceals a lack of analytical depth. Roughly a third raised algorithmic bias: because models are trained on historically skewed datasets, they reproduce existing hierarchies of knowledge and privilege dominant discourses while sidelining minority viewpoints, emerging theories, and context-specific interpretations. “The real danger of AI in research is that it often reinforces biases in its responses,” one sociology professor warned. The concern is epistemic as much as technical — unchecked, bias shapes which questions get asked, which evidence surfaces, and whose scholarship becomes visible.

Ethical questions dominated the interviews, and none proved thornier than accountability. “Who is responsible when AI generates false information in a research paper?” a philosophy faculty member asked. “The ethical implications are unclear.” Participants converged on a clear answer in principle: the human researcher who uses and submits the content carries the responsibility, because AI cannot exercise moral judgment, justify methodological decisions, or answer for scholarly claims. That position mirrors the authorship policies of major publishers, which bar AI from authorship on the grounds that authorship demands accountability and intentionality that machines lack. Yet most participants found current institutional policies dangerously vague, and an engineering professor’s warning — “Universities need strict policies on AI use, or else we risk a crisis of academic dishonesty” — reflected widespread anxiety about AI-generated text passing as original work. More than half favored mandatory disclosure whenever AI contributes to idea generation, writing, or data analysis, reflecting a broader reconceptualization of academic integrity from simply protecting originality toward governing the entire pipeline of AI-assisted knowledge production.

Participants also worried about a quieter casualty: scholarly voice. More than two-thirds identified the homogenization of academic writing as a significant risk, with a literature scholar complaining that AI-written papers “sound generic” and “lack the nuance that comes from real scholarly engagement,” and a historian fearing that AI “will gradually erase individual academic voices.” More than half said AI-generated writing lacks a personal scholarly perspective, while about 39 percent worried that continued advances could eventually diminish the role of human authors altogether. The technical explanation lies in how language models work: they predict the most statistically probable response given the patterns in their training data, which structurally favors consensus over novelty and convention over intellectual divergence. Participants framed the stakes in strikingly epistemic terms — drafting, revising, and organizing arguments are not merely acts of communication but part of how researchers develop their own thinking, so outsourcing them risks converting intellectual discovery into the editing of machine-generated content, thinning the conceptual diversity on which scientific progress depends.

Perhaps the study’s most striking statistic is its near-unanimity on governance: 97 percent of participants said the absence of clear institutional policies has left researchers navigating AI with individual judgment rather than established standards. “We need a structured way to integrate AI into research. Right now, it’s a free-for-all tool,” one computer scientist complained. Roughly two-thirds called for formal AI literacy training covering both the capabilities and the limitations of these systems, ethical considerations, responsible research practices, and critical evaluation of AI-generated content. “Professors and students need AI training to use it responsibly,” a business studies participant insisted. That consensus aligns with influential work in AI ethics, including Floridi and Cowls’s framework emphasizing transparency, accountability, fairness, and meaningful human oversight, and with the research priorities articulated after ChatGPT’s emergence in journals such as Nature. Participants, in other words, were not demanding prohibition — they wanted clearer institutional expectations defining where machine assistance is legitimate and where human responsibility cannot be delegated.

The answer Shahini proposes is HAKI, a framework built from three interlocking domains and four operational processes. The domains are Human-Centric Knowledge — the tacit and explicit knowledge generated by human cognition, experience, and creativity; AI-Assisted Knowledge — structured, retrieved, or synthesized data produced by algorithms, large datasets, and pattern recognition; and Ethical-Regulatory Frameworks — the guidelines that govern how the two interact. The processes turn those abstractions into workflow. Human Analytical Control requires researchers to define the task before touching the tool: specifying keywords, date ranges, inclusion and exclusion criteria, and quality thresholds so that AI use is purposeful and bounded. AI Ethical Mediation mandates open disclosure of any significant AI assistance, along with logging of prompts, model versions, and parameters to create an auditable trail, with AI credited as a tool rather than an author. Critical Interpretation obliges scholars to treat AI output as raw material to be triangulated against primary sources and woven into original analysis. Recursive Verification closes the loop: every fact, citation, and data point is checked against primary evidence, and failed checks send the researcher back to earlier stages.

Conceptually, HAKI is anchored in Sociotechnical Systems Theory, which holds that technological and human elements should be jointly optimized for both effectiveness and ethical responsibility, and in Epistemic Agency Theory, which insists that knowledge ultimately depends on human judgment, evaluation, and accountability. Shahini argues the model fills a hole left by better-known theories: the SECI model of knowledge creation cannot accommodate AI, which lacks the lived experience and intentionality needed for genuine tacit knowledge; Actor-Network Theory grants technologies an agency that cannot exercise consciousness or moral judgment; and the Extended Mind thesis fails to distinguish passive cognitive tools from autonomous generative systems. HAKI instead positions AI as a “dependent cognitive collaborator” inside a human-centered knowledge ecosystem — neither an autonomous knowledge creator nor a mere calculator. It also aims to move beyond existing human-centered initiatives such as human-in-the-loop oversight, Google’s People + AI Research design guidebook, and the ethical principles issued by UNESCO, the OECD, and the European Commission’s high-level expert group, which, Shahini contends, articulate ideals without providing the executable, discipline-sensitive procedures that academic writing actually requires.

The author is candid that HAKI remains a conceptual and exploratory framework, not an empirically validated operating model, and that its contribution is theoretical — a foundation for future investigation rather than a finished product. The framework assumes human oversight can effectively regulate AI-generated knowledge, yet as models grow more sophisticated, their outputs become harder to distinguish from authoritative scholarship, and subtle biases may evade even diligent verification. Enforcing ethical-regulatory frameworks across institutions, publishers, and disciplines that lack standardized disclosure policies poses another hurdle, compounded by uneven AI literacy among researchers. And the model does not yet grapple with what comes next: AI systems that generate original hypotheses, interpret complex data autonomously, or simulate critical reasoning. Shahini’s roadmap calls for AI-assisted fact-checking tools, comparative studies of institutional disclosure policies, standardized authorship guidelines, and the embedding of AI education into research methods training. Until then, the framework’s message is stark and simple: the more machines can do, the more human accountability matters.

Subject of Research: Faculty perceptions of AI-assisted academic research and the development of the HAKI conceptual framework for ethical human–AI collaboration in knowledge production

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02041-4

Keywords: Artificial intelligence, Academic research, Authorship, Knowledge production, AI ethics, HAKI model, Academic integrity, AI literacy