Conference Agenda

This presentation addresses the 2026 EdTech conference theme by centering the lived experiences of the "who" in our digital learning ecosystems: the students. Supported by generous funding from the national N-TUTORR project, this Masters research is based on a mixed-methods study of 282 survey respondents and 15 interviewees across four Irish Technological Universities. Ultimately, this presentation explores how students interact with Generative Artificial Intelligence (GenAI) and advocates for a human-centric, ethical approach to its integration.

Part 1: Key Findings on Student GenAI Usage The study reveals a pragmatic approach to GenAI among students, with the majority reporting moderate to high familiarity with the technology. The primary motivations for using GenAI are to save time and increase efficiency (63.8%), and to better understand complex coursework (49.6%). Despite narratives of widespread uncritical adoption, students currently use GenAI predominantly for auxiliary tasks like study assistance, grammar refinement, and brainstorming, rather than direct content generation. However, students harbour profound concerns about the technology; 76.6% worry about content accuracy and "hallucinations," while substantial majorities fear that GenAI may perpetuate societal biases and diminish independent critical thinking.

Part 2: Ethical Guidance, Academic Integrity, and GenAI as a Tool. Academic integrity remains a highly emotive issue, with 77% of students agreeing that GenAI increases academic dishonesty and makes it easier to submit unoriginal work. However, students are deeply frustrated by the "grey area" of current institutional responses and the unreliability of AI detection software, which frequently causes false flagging and distrust. To ensure academic integrity, institutions must stop viewing GenAI solely as a threat and instead provide explicit, structured guidance to help students use it ethically as an "assist tool". By moving away from blanket bans and focusing on transparency—such as requiring students to document their GenAI use and prompt engineering—educators can guide learners to harness GenAI to legitimately enhance and support their academic work.

Part 3: The Psychological Perspective: GenAI, Motivation, and Self-Efficacy. From a psychological perspective, GenAI intersects deeply with student motivation and learning approaches. The study found a strong correlation between motivational profiles and GenAI adoption: "strategic" and "surface" learners readily utilise GenAI to manage academic demands and time constraints, whereas intrinsically motivated "deep learners" are significantly more hesitant to use it. Beyond study habits, GenAI has a profound psychological impact by boosting student confidence, reducing academic anxiety, and acting as a scaffold for self-regulated learning. It serves as a powerful accessibility tool—functioning as an "ADHD assistant" for neurodivergent students, helping non-native speakers overcome language barriers, and providing a judgment-free space for shy students to ask questions.

Part 4: Recommendations for the EdTech Community To responsibly integrate GenAI and prepare students for an AI-permeated future, this presentation concludes with four student-informed recommendations:

1. Redesign Assessments for Authentic Learning: Shift away from traditional essays susceptible to AI generation and move towards process-oriented assignments, in-class research, and oral defences (Vivas) that measure genuine understanding.

2. Cultivate AI Literacy and Critical Thinking: Implement mandatory onboarding modules that explicitly teach students how to fact-check AI outputs, craft precise prompts, and evaluate algorithmic bias.

3. Shift from Detection to Trust-Based Verification: Abandon unreliable AI detection tools in favour of transparent assignment portals where students document their human-AI collaboration and demonstrate their critical thinking processes.

4. Co-Create Clear Policies: Develop flexible, module-specific guidelines in partnership with students, transforming the educational culture from one of suspicion to one of collaborative, ethical engagement.

As GenAI becomes increasingly embedded in higher education, a central question is who retains epistemic agency. This paper reports on the human-centred design of an undergraduate module in Database Technologies. Drawing on human-centred AI principles (Shneiderman, 2020) and Students-as-Partners scholarship (Cook-Sather et al., 2014), it argues that ethical AI integration requires deliberate epistemic friction. Rather than treating AI simply as a tool for efficiency, the pedagogical approach framed it as a contestable cognitive partner, emphasising students’ capacity to question, constrain, and override its outputs.

The study involved 62 students, 70% of whom reported no prior knowledge of the subject material. The module began with participatory design: students co-created and prioritised requirements for a fictional travel information system using in-class polling tools. The lecturer then demonstrated how to translate these requirements into a database schema through iterative dialogue with ChatGPT. In keeping with the concept of cognitive apprenticeship (Collins et al., 1989), expert reasoning was made explicit and chat dialogues were shared with students as artefacts of the process.

Subsequent tuition, alongside weekly lectures, incorporated online revision quizzes and a custom-designed chatbot featuring learning-from-errors diagnostic modules, query explanation, adaptive feedback, and formative MCQs. The end-of-semester test also used MCQ format; however, students were required to provide written rationales rather than simply select an answer, with additional marks awarded for correct explanations. This design operationalised the self-explanation effect (Chi et al., 1994) and ensured that correctness remained attached to reasoning.

To examine whether AI integration scaffolded or displaced independent competence, this study distinguished between AI-assisted and unassisted self-efficacy in the technical material, drawing on Bandura’s (1997) theory of agentic capability. This distinction extends technology acceptance frameworks (Venkatesh et al., 2003) by shifting attention from adoption and perceived usefulness toward epistemic calibration; that is, the learner’s capacity to judge when AI should be trusted, challenged, or overridden.

Survey data (n = 47) revealed a nuanced pattern. Students widely recognised the utility of AI. 85% agreed that AI tools increased their confidence in tackling the material, 89% reported meaningful learning benefits from the chatbot, and 85% felt comfortable trusting it despite awareness that it might produce incorrect outputs. However, indicators of independent competence were more differentiated. While 53% agreed they could write database queries without AI assistance, only 40% agreed they could independently troubleshoot errors. At the same time, 53% reported confidence in adjudicating disagreements between themselves and AI. Taken together, these findings suggest calibration rather than capitulation: students appear willing to use AI as a support tool while still regarding human judgement as the ultimate arbiter of correctness.

Behavioural data from the revision quizzes (n = 53) reinforce this interpretation. Students were permitted unlimited attempts, and performance improved markedly across attempts. In many cases, initial scores were very low, yet students frequently reached 85–100% after repeated attempts. These trajectories suggest engagement patterns more consistent with mastery-oriented revision than with one-shot answer extraction. If students were primarily using AI to generate answers directly, the expectation would be to see rapid convergence on correct responses with few attempts. Instead, the data show iterative cycles of error, feedback, and correction. This pattern aligns with the “learning from errors” pedagogy embedded in the chatbot and classroom approach, suggesting that AI functioned less as an answer engine and more as a diagnostic scaffold supporting repeated practice, thereby supporting the human-centred aim of maintaining students’ epistemic agency.

References

Bandura (1997). Self-efficacy. Freeman.

Chi (1994). Self-explanations improve understanding. Cogn Sci, 18(3).

Collins (1988). Cognitive apprenticeship. Thinking, 8(1).

Cook-Sather (2014). Engaging students as partners. Wiley.

Shneiderman (2020). Human-centered AI. Int J HCI, 36(6).

Venkatesh (2003). User acceptance of IT. MISQ, 27(3)..

Artificial intelligence (AI) is rapidly transforming educational environments, offering new possibilities for personalised learning, feedback generation, and administrative efficiency. Research highlights the potential of AI to support teachers’ workload, enhance learning analytics, and facilitate adaptive learning experiences (Kumari et al., 2025). At the same time, concerns remain regarding teacher preparedness, ethical use, data privacy, and the broader implications of AI for educational quality (Prilop et al., 2024). As schools increasingly experiment with AI tools, understanding how educators adopt and perceive AI is essential to inform implementation strategies and professional development initiatives.

Method

This study explores the adoption, perceived impact, and trust in AI among educators across Ireland. Data come from a quantitative online survey conducted between March and April 2025 with 201 primary and secondary educators in the Republic of Ireland (ROI, 60%) and Northern Ireland (NI, 40%). In the sample, 59% worked in primary schools and 41% in secondary schools. The survey examined AI usage frequency, perceptions of its impact on learning, and trust in AI technologies.

The study addressed 3 research questions:

RQ1: What are the current adoption trends in generative AI use among educators?

RQ2: How do educators perceive the impact of AI in education?

RQ3: What is the level of trust in AI technologies, and what are the primary concerns?

Results

RQ1.Current adoption trends in generative AI use

Educators were asked whether their use of different teaching technologies had increased over the previous 12 months. Generative AI showed the largest increase, with 74% reporting increased use. ChatGPT (52%) and Gemini / Google Classroom (44%) were the most widely used tools, indicating growing integration of conversational AI and AI-supported learning platforms into teaching practices.

Among teachers using AI with students, most did so regularly, typically once per week (66%). Primary teachers (74%) were more likely than secondary teachers (54%) to use AI regularly, suggesting adoption patterns differ across educational contexts.

RQ2.Educators’ perceptions of AI impact

Educators’ perceptions reveal a “positivity paradox.” While 72% agreed that AI use in classrooms should increase to support learning and 64% believed it will positively transform education, 57% also expressed concern that AI could decrease educational quality. These findings indicate that teachers recognise AI’s potential to support personalised feedback and learning, yet remain cautious about its broader pedagogical implications and long-term effects.

RQ3. Trust in AI and concerns

Most educators reported trusting AI “to some extent” (60%), with 25% fully trusting it and 11% not trusting it. Regarding reasons for lack of trust, data privacy was the most frequently cited issue (45%), particularly in primary schools (55% vs. 35% in secondary schools). Ethical considerations were also a concern (38%), including responsible use, student autonomy, and the development of critical thinking skills, and were more often reported in secondary education (43% vs. 34% in primary).

Conclusion

Generative AI is rapidly gaining traction in schools, with many educators integrating AI tools into their teaching. However, teachers’ perceptions reflect a balance between optimism and caution, highlighting benefits for personalised learning and efficiency alongside concerns about privacy, ethics, and educational quality. These results underscore the need for targeted professional development and institutional guidance to support the responsible and effective adoption of AI technologies in educational settings.

References

Kumari, D. A., Begum, D. S., Paunikar, M. S., Kaur, A. & Verma, D. S. (2025). The Role of Artificial Intelligence in Teacher Training: Enhancing Pedagogical Effectiveness. Journal of Marketing & Social Research, 2(5), 116-122.

Prilop, C. N., Mah, D.-K., Jacobsen, L. J., Hansen, R. R., Weber, K. E., & Hoya, F. (2024, December 19). Generative AI in teacher education: Using AI-enhanced methods to explore teacher educators’ perceptions. https://doi.org/10.31219/osf.io/szcwb