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Between Promise and Practice: Bridging Ethical Artificial Intelligence Literacy Gaps Across Students, Educators, and Policy

◐ medium Β· 2026-08-20 Β· ai-literacy, ethics, generative-ai, higher-ed, teacher-role, educational-policy-ai

Synthesis: Fekete (2026) synthesizes two European questionnaire studies β€” one with students (n = 226) and one with instructors (n = 256) β€” that used parallel five-point scales to measure AI Literacy, motivation, Ethics, intention, and institutional support in Higher Ed. Independent-samples t-tests revealed significant group differences: students rated their ethical awareness higher than instructors, while instructors reported stronger willingness to experiment with Generative AI tools and higher behavioural intention β€” in part because students view Ethics through their immediate coursework practices, whereas teachers interpret it through a lack of institutional clarity and treat it as a question of integrity. Cluster analysis of instructors identified three user groups differing across literacy dimensions, with the Ethics scale providing the clearest distinction, and correlation analyses showed that instructors' moral awareness grows with institutional and social support while students' confidence and readiness correlate mostly with self-efficacy and collaboration rather than formal instruction.

Key Findings

  • Students report higher ethical awareness than instructors: the largest gap in the study, with students averaging 4.03 versus instructors' 2.44 on ethical awareness (t(480) = 18.143, p < .001). Students tend to frame Ethics as fairness within immediate coursework, while instructors treat it as a question of institutional clarity, human oversight, and academic integrity.
  • Instructors report stronger willingness and behavioural intention to use AI: instructors scored higher on willingness (t(480) = βˆ’3.879, p < .001) and intention (t(480) = βˆ’9.643, p < .001), yet perceived their own ethical confidence as low β€” a "ready but hesitant" profile that reflects the absence of institutional guidance.
  • Both groups report weak institutional support: students rated external support low (M = 2.19) and described largely self-directed, informal AI learning; instructors perceived stronger (yet still limited) support through training or peer exchange, highlighting a responsibility gap in Educational Policy AI.
  • Cluster analysis of instructors reveals three distinct profiles: k-means clustering identified Beginner (24%), Intermediate (56%), and Advanced (20%) users based on AI-TPACK dimensions, with the Ethics scale discriminating most strongly between clusters (F = 124.073, p < .001) β€” ethical awareness rises as professional readiness advances.
  • Support drives instructor readiness, peers drive student readiness: for instructors, ethics correlates with facilitating conditions (r = .322) and behavioural intention (r = .353); for students, self-efficacy correlates with willingness (r = .730) and collaboration (r = .653), indicating readiness arises from confidence and peer interaction rather than formal instruction.
  • Engagement is asymmetric: students use AI mainly for surface tasks (brainstorming 38.1%, short homework 36.3%, information search 32.7%), while instructors use LMS in 71.1% of classes but only 10.9% mention AI and fewer than 6% actively demonstrate it β€” students expect guidance teachers have not yet developed.

Study Design & Method

This is an empirical synthesis applying a comparative and interpretive design (Yanow, 2014) to two related, non-representative convenience-sample questionnaire studies conducted in 2024 across the European Higher Ed context. The student study (n = 226; mean age 24.96; largest cohorts from Hungary and Austria) used a 15-scale instrument measuring components of AI Literacy and attitude, while the instructor study (n = 256; mean age 47.14; 18.6 years teaching experience) used 18 multi-item scales organized into three blocks: the seven TPACK domains, four AI-TPACK extensions, and seven AI literacy/behavioural constructs. All scales used five-point Likert items and met reliability standards (Cronbach's Ξ± β‰₯ .70). Student data were analysed with independent-samples t-tests; instructor data additionally supported k-means cluster analysis (with ANOVA and post-hoc Duncan tests) and Pearson correlations. Participation was voluntary and anonymous with ethical approval (Refs #2024/02/3 and #2024/02/4), and analyses were run in SPSS 22 with significance accepted at p < .05.

Implications for AI in Education

The paper reframes responsible Generative AI use as a shared responsibility distributed across learners, educators, and institutions rather than an individual competence. Its central insight β€” that students' ethical awareness and instructors' readiness develop through different mechanisms β€” implies that AI Literacy cannot be fostered by one-size-fits-all training: students need guidance, structured feedback, and teacher presence to convert informal experimentation into informed behaviour, while instructors need clear, context-sensitive institutional frameworks and professional development. The finding that instructors' moral awareness grows with institutional and social support (while students' readiness hinges on self-efficacy and collaboration) directly implicates Educational Policy AI, calling for universities to strengthen internal communication and provide clear points of contact for AI-use questions. The work also suggests reimagining assessment so ethical AI use is evaluated through process, reflection, and judgment rather than detection alone, connecting to Higher Ed debates on integrity and AI-Act governance.

Connected Concepts

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Citation

Fekete, I. (2026). Between promise and practice: Bridging ethical artificial intelligence literacy gaps across students, educators, and policy. Journal of University Teaching and Learning Practice, 23(5).