Student Perspectives on Teacher Interpersonal Behaviours in <scp>AI</scp> ‐Mediated Higher Education: A Q Methodology Study

ABSTRACT Research on artificial intelligence (AI) in higher education largely emphasises technical performance and outcomes, leaving student views of teacher interpersonal behaviours in AI‐mediated classrooms insufficiently understood. In response, this study applied Q methodology, supported by content and sentiment analyses, to examine student perspectives. A total of 30 university students Q‐sorted 40 statements, covering empathy, immediacy, clarity, autonomy support, engagement and personalization. Subsequently, principal component analysis with varimax rotation yielded four factors: (F0) Selective‐Connection Seekers, (F1) Autonomous Collaborators, (F2) Gamification‐Driven Minimalists and (F3) Clarity and Empathy Advocates. Additionally, open‐ended reflections clustered into seven themes, with Clarity and Instructional Precision and Empathy and Emotional Support most frequent. At the same time, sentiment analysis showed predominantly positive–neutral tones, with some resistance to over‐personalization. Overall, the findings indicated that authentic interaction, transparent criteria and profile‐aligned differentiation (including targeted gamification) are pivotal for sustaining meaningful teacher–student relationships. Accordingly, several actionable suggestions were provided based on the findings.

Authors

Institutions

Publication Details

Journal
European Journal of Education
Published
2025-10-17
DOI
https://doi.org/10.1111/ejed.70293
Primary Topic
Education Practices and Challenges
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Student Perspectives on Teacher Interpersonal Behaviours in AI ‐Mediated Higher Education: A Q Methodology Study

Kaveh Jalilzadeh, Ferdi Çelık, Volkan Duran
European Journal of Education
Education Practices and Challenges
article

Student Perspectives on Teacher Interpersonal Behaviours in AI ‐Mediated Higher Education: A Q Methodology Study

Kaveh Jalilzadeh, Ferdi Çelık, Volkan Duran
article en

Abstract

ABSTRACT Research on artificial intelligence (AI) in higher education largely emphasises technical performance and outcomes, leaving student views of teacher interpersonal behaviours in AI‐mediated classrooms insufficiently understood. In response, this study applied Q methodology, supported by content and sentiment analyses, to examine student perspectives. A total of 30 university students Q‐sorted 40 statements, covering empathy, immediacy, clarity, autonomy support, engagement and personalization. Subsequently, principal component analysis with varimax rotation yielded four factors: (F0) Selective‐Connection Seekers, (F1) Autonomous Collaborators, (F2) Gamification‐Driven Minimalists and (F3) Clarity and Empathy Advocates. Additionally, open‐ended reflections clustered into seven themes, with Clarity and Instructional Precision and Empathy and Emotional Support most frequent. At the same time, sentiment analysis showed predominantly positive–neutral tones, with some resistance to over‐personalization. Overall, the findings indicated that authentic interaction, transparent criteria and profile‐aligned differentiation (including targeted gamification) are pivotal for sustaining meaningful teacher–student relationships. Accordingly, several actionable suggestions were provided based on the findings.

European Journal of EducationVol. 60(4)
Ondokuz Mayıs University (TR), Istanbul University-Cerrahpaşa (TR), Software Research and Development Consulting (TR)
Openalex Percentile: Top 72%
Education Practices and Challenges
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Student Perspectives on Teacher Interpersonal Behaviours in <scp>AI</scp> ‐Mediated Higher Education: A Q Methodology Study — Kaveh Jalilzadeh, Ferdi Çelık, et al. · European Journal of Education (2025) | TGRS Research Map | TGRS