Students’ Perceptions: Exploring the Interplay of Ethical and Pedagogical Impacts for Adopting AI in Higher Education
Open accessAbstract
Artificial intelligence is increasingly implemented in higher education, offering customized interventions and timely feedback to enhance learning experiences. While these tools have the potential to improve educational outcomes, they also introduce ethical risks and sociotechnical implications such as reduced learner autonomy. Current ethical discussions often focus on computational issues and overlook the nuanced impacts from students’ perspectives, which may increase students’ vulnerability. Taking a student-centered approach, we apply the Story Completion Method to investigate students’ concerns about adopting analytics-based AI tools in education. Seventy-one participants responded to the story prompts, which we analyzed qualitatively to uncover perceptions about how these tools may reshape pedagogical aspects such as learner autonomy, learning environments and approaches, interactions and relationships, and pedagogical roles. Our findings reveal that these potential impacts not only occur in isolation but also interact with one another. This study makes two primary contributions: first, it marks a novel application of the speculative design method to explore students’ perceptions of AIEd tools. Second, it provides a qualitative analysis of key themes derived from student responses, offering design implications for AIEd systems that are sensitive to student concerns and ethical considerations. These insights offer a foundation for future research and contribute to a more student-centered approach to the ethical development of AIEd.
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Existing technology acceptance scales were not suitable to capture the specific affective and pedagogical concerns raised in prior observational and qualitative studies of AIEd systems.
“Existing technology acceptance scales (e.g., [73]) were not suitable to capture the specific affective and pedagogical concerns raised in prior observational and qualitative studies of AIEd systems (e.g., [53, 56]).”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [56]
The mechanism of sensing that promises personalised, responsive teaching also risks undermining students' learning experiences.
“Thus, the very mechanism of sensing that promises personalised, responsive teaching also risks undermining students' learning experiences [53, 56].”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [56]
Autonomy in education also encompasses broader control over what and how one studies, and the ability to make learning choices without undue direction from technologies.
“As both prior research [56] and our qualitative fi ndings emphasise, autonomy in education also encompasses broader control over what and how one studies, and the ability to make learning choices without undue direction from technologies [56].”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [56]
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- Authors
- Han, B., Nawaz, S., Buchanan, G., & McKay, D.
- Affiliations
- The University of Melbourne (AU)
- RMIT University (AU)
- Corresponding author
- Han, Bingyi
- Venue
- International Journal of Artificial Intelligence in Education
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 35
- Issue
- 4
- Pages
- 1887-1912
- ISSN
- 1560-4292 (ISSN-L)
- OpenAlex
- W4406230992
- Cited by
- 32
- References
- 66
- Citations by year
- 2026: 21 · 2025: 11
- FWCI
- 33.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Artificial Intelligence in Education (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, student perceptions, AI ethics, learner autonomy, speculative design, student-centered design, pedagogical role, learning environment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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