Large language models have swept through higher education faster than any technology before them, and a new study suggests the people using them most intensively may trust them least. A survey of 80 higher education professionals, most based in Nordic countries, has documented what its authors call the adoption–trust paradox: educators overwhelmingly use AI tools in their daily work while reporting strikingly low confidence in what those tools produce. The finding, published in the journal AI & Society, reframes the debate about generative AI in universities, shifting attention away from student cheating and toward a quieter, more structural problem — institutions that have let practice run far ahead of governance.
The numbers tell a contradictory story. In the self-selected sample, 82.5 percent of respondents reported using LLMs in teaching and teaching-related activities, and they rated the benefits of these tools highly, with a mean score of 3.93 out of 5. They were even more convinced that their students use AI regularly, scoring that belief at 4.19, the highest mean in the entire survey. Yet trust in LLM outputs scored just 2.45 out of 5. Privacy concerns registered at 3.82, and confidence in distinguishing student-written work from machine-generated text sat at a meager 2.58. The lowest score of all, 1.99, went to using LLMs for grading student work — a result the authors read as deliberate professional caution in the highest-stakes corner of academic practice.
What makes this pattern genuinely paradoxical, the researchers argue, is that it defies a well-established expectation from the international literature. Studies of college students, including large-scale work on ChatGPT attitudes, have consistently found that familiarity breeds comfort: the more students use AI tools, the more positive their attitudes and the stronger their intent to use them responsibly. Among educators, that relationship collapses. An exploratory correlation between LLM familiarity and trust in this sample was effectively zero, and years of experience showed a weak negative association with trust, tentatively suggesting that the most seasoned educators trust the tools somewhat less, not more.
The authors attribute this divergence to position and accountability. Students often treat LLMs as a shortcut to finished work; educators must answer for assessment integrity, teaching quality, and the credibility of the qualifications their institutions award. That responsibility makes them acutely alert to what the tools get wrong. The Nordic setting may amplify the effect rather than dampen it. In systems built on high institutional trust, academic freedom, and student autonomy, a technology that muddles verification and accountability may land harder than in systems already accustomed to surveillance-based quality checks. Trust, the study suggests, does not come from knowing the tools better — it comes from institutional structures that make verification, accountability, and recourse real.
Underpinning the paradox is what the researchers call institutional uncertainty: educators routinely using a technology they do not trust, without adequate policies, governance structures, or assessment frameworks to manage the risks. The survey itself did not directly measure institutional governance, and the authors are careful to flag their governance-failure interpretation as an inference. But it is one anchored in external evidence. Multi-country Nordic policy studies have found institutional responses ranging from comprehensive AI guidelines at one end to little more than a reference to existing integrity norms at the other, with nothing specific about LLMs. In practice, each educator ends up drawing the ethical line alone.
The study models the paradox as a self-reinforcing six-stage loop. It begins with perceived necessity — students are already using these tools, so opting out feels unrealistic — which drives high adoption. Adoption brings repeated exposure to outputs that are unreliable or hard to verify, which coexists with low trust. Low trust, in turn, coincides with the absence of clear institutional rules, and that absence pushes educators back onto personal judgment, normalizing further use and restarting the cycle. The authors stress that this is a conceptual synthesis of co-occurring patterns, not a statistically tested causal chain, and they present it as hypothesis-generating. But if it holds, the implication is sobering: adoption alone will never build trust, and the plausible points of intervention are the policy and uncertainty gaps at the end of the loop.
Nowhere is the governance gap more visible than in assessment. Nearly half of respondents — 47.5 percent — named academic integrity as the foremost challenge of AI integration, and the clustering of that concern with adapting assessment methods (38.8 percent) and maintaining critical thinking (40.0 percent) suggests these are experienced as interconnected systemic problems rather than isolated disciplinary issues. Respondents reported that students rarely reveal their LLM use, and their own confidence in detecting AI-assisted work was low. The problem, the authors argue, is not simply that students may misuse the tools; it is that the assessment infrastructure lacks the capacity to function meaningfully when the boundary between human and machine-generated work has blurred. Recent reviews support this shift in framing, moving from generative AI as a cheating tool to generative AI as a systemic challenge demanding redesigned assessment and institutional policy.
Regulation is beginning to catch up, at least on paper. Under the EU AI Act, AI systems used to determine access to educational institutions, evaluate learning outcomes, or assess appropriate levels of education are classified as high-risk, triggering requirements for transparency, human oversight, and conformity assessment. The near-total avoidance of LLMs for grading in this sample may partly reflect an intuitive recognition of those stakes. But the broader pattern of informal, ungoverned adoption across other academic activities suggests most institutions have not yet built the governance infrastructure that emerging regulatory expectations will demand. The authors also point to the gap between perceived competence and actual trust — respondents rated LLMs moderately competent in their own fields at 3.01, yet trusted outputs at only 2.45 — as evidence that trust depends on governance, transparency, and institutional backing rather than technical performance alone.
Interestingly, educators are not waiting for permission to rethink pedagogy. When asked which skills matter most in an AI-enhanced environment, they ranked critical thinking first, followed by information literacy and ethical reasoning — priorities that read as the constituent elements of what the broader literature calls AI literacy. Their favored teaching approaches, project-based and collaborative learning, point toward process-oriented methods in which the production of text alone is no longer a reliable indicator of student competence. Yet this pragmatic pedagogical reasoning is happening without coordination: individual educators are independently arriving at similar conclusions, without shared frameworks or institutional support.
The study’s limitations deserve emphasis. The sample of 80 is modest, recruited through open channels that likely over-represent educators already interested in AI, making the 82.5 percent adoption figure an upper bound rather than a prevalence estimate. The sample skews toward Finland and Norway, toward experienced staff with doctoral degrees, and toward the social sciences and humanities. A descriptive comparison showed Nordic and non-Nordic respondents reported near-identical trust levels, suggesting the pattern is not a Nordic artifact, but generalizability remains limited. Still, the consistency of the pattern across every section of the survey makes it hard to dismiss. The authors’ conclusion is a call for structural reform rather than behavioral adjustment: explicit institutional strategies clarifying acceptable use, responsibility, and accountability, developed with educators and students rather than imposed as top-down compliance. Trustworthy AI in education, they argue, is not a property of the technology but a socio-technical achievement that emerges from governance, transparency, and shared understanding — and right now, the institutions that certify human knowledge are leaving that achievement to individuals.
Subject of Research: The adoption–trust paradox in large language model use among higher education professionals and its implications for institutional AI governance
Article Title: Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems
Article References: Kringen, P., Gallucci, A., Hildt, E., Johansen, F. R., Westerlund, M., & Tigerstedt, C. (2026). Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03323-z
Image Credits: AI Generated
DOI: 10.1007/s00146-026-03323-z
Keywords: large language models, higher education, academic integrity, institutional governance, trust in AI, adoption–trust paradox, assessment, Nordic higher education, EU AI Act, AI literacy, educator survey, AI & Society
News Source: Courtney Benton. (October 7, 2026). Educators Use AI They Don’t Trust, and Institutions Are Letting It Happen. Scienmag.
