Jun 2026· Research on Education and Media· Vol 18, pp. 63 - 73· 0 citations· 27 references
TL;DR
This study explores faculty and student perceptions of AI knowledge, training, and ethical use within a higher education context and suggests opportunities for AI literacy initiatives and continued discussion regarding ethical AI use in higher education.
Abstract
Abstract In Spring 2025, librarians at a regional comprehensive university in Southwest Florida conducted focus groups with faculty and students to explore their perceptions and experiences with artificial intelligence (AI). Previous research suggests that both groups recognize potential benefits of AI for learning, teaching, and productivity while expressing concerns regarding misinformation, ethics, and future societal impacts. The purpose of this study was to explore faculty and student perceptions of AI knowledge, training, and ethical use within a higher education context. Using a phenomenological approach, researchers conducted separate focus groups with faculty and students and analyzed the resulting discussions for recurring themes. Faculty participants expressed concerns related to critical thinking, institutional guidance, access to practical training, and adapting instruction to an AI -enabled environment. Student participants emphasized misinformation, future employment, and broader societal impacts of AI. Participants in both groups viewed AI as a tool that could be used ethically when applied appropriately. Although limited by its small sample size and single-institution context, the findings suggest opportunities for AI literacy initiatives and continued discussion regarding ethical AI use in higher education.
The study found that AI-focused professional development significantly enhanced faculty knowledge and comfort with AI integration among the 100 faculty members who participated in the study, and revealed a critical gap in ethical AI knowledge.
Jaime Januse, S. Jindal, Stephen Gregoire· AI-Enhanced Learning· 0 citations
This study, conducted using an explanatory sequential mixed-methods design, aims to examine the views of faculty members and students on ethical issues related to the use of artificial intelligence (AI) in higher education. In the quantitative phase, data were collected via a survey from 971 students and 135 faculty members, followed by semi-structured interviews with 23 students and 14 faculty members to obtain qualitative data. The findings show that both groups generally expressed supportive views regarding the ethical use of AI, but these views took different forms at the individual, technological, institutional, and societal levels. Faculty members emphasized the importance of ethical principles but pointed out the lack of institutional guidelines and support. Students, on the other hand, stated that AI tools were beneficial in their learning processes, but expressed uncertainty about the sharing of ethical responsibilities and the assessment of the ethical appropriateness of the processes. Qualitative findings showed that perspectives were shaped in six themes, revealing a multidimensional ethical structure in the use of AI in education. Participants emphasized that while AI facilitates learning, its excessive use can have negative effects on cognitive skills. The results indicate that, in order for AI to be used ethically in higher education, professional development support should be provided to faculty members, courses covering ethical dimensions should be added to curricula, clear ethical usage guidelines should be established at universities, and these guidelines should be updated through interdisciplinary collaboration.
Buket Bilgiç, Demet Sever· International Journal of Edu...· 0 citations
This paper explores expert perspectives on the integration of artificial intelligence (AI) in higher education and proposes a preliminary Student-AI-Centered conceptual framework. While AI is increasingly deployed in teaching and learning contexts, the field currently lacks conceptual frameworks that intentionally balance student agency with AI-enabled support. This study addresses that gap by examining how educators and institutions can promote responsible, ethical and student-driven AI adoption.
A qualitative research design was employed, drawing on semi-structured interviews with educational technology experts selected through purposive sampling. Each participant had a minimum of five years of experience in AI-related practice within higher education. Data were analysed using Braun and Clarke’s (2006) six-phase thematic analysis framework, with trustworthiness strengthened through member checking, triangulation, peer debriefing and an audit trail.
Thematic analysis of expert interviews produced two overarching themes: (1) Potentials of AI in Higher Education, encompassing three sub-themes, personalised learning, inclusive education and enhanced learning participation and (2) Challenges of AI Integration, encompassing two sub-themes, unverified information and ethical compliance. Drawing on these themes, the study proposes a seven-component Student-AI-Centered conceptual framework that integrates AI into curricula while emphasising ethical awareness, diverse assessment modalities and ongoing educator support.
The exploratory nature of the study and the small, purposive sample limit the generalisability of the findings. Future research should validate the proposed conceptual framework through larger, cross-institutional studies and longitudinal designs that assess its applicability across diverse educational settings and cultural contexts.
The Student-AI-Centered conceptual framework offers educators and institutions practical guidance for integrating AI tools responsibly. Pedagogical integrity refers to maintaining the primacy of genuine learning outcomes by ensuring AI supplements rather than supplant critical reasoning, independent inquiry and authentic assessment. Promoting critical thinking means equipping students to interrogate, verify and evaluate AI-generated content rather than accepting it uncritically. Together, these principles, alongside fostering ethical awareness, aim to cultivate responsible, reflective AI users in higher education.
This paper contributes a novel conceptual framework that positions AI as a complementary agent within student-centered learning, rather than as a replacement for educators or a source of uncritical dependency. The Student-AI-Centered approach bridges the gap between teacher-centered, student and AI-centered paradigms.
Annisa Fitri, Hamza Yusuf, Abdur Razzaq et al.· Education Innovations: Syste...· 0 citations
Student Perceptions of Instruction (SPoI) surveys play a central role in evaluating teaching effectiveness in higher education, informing instructional improvement, faculty development, and institutional decision-making. Despite their widespread use, limited research has explored how graduate students interpret and engage with these evaluations. This qualitative case study examined how graduate students perceive the internal motivators, external pressures, and survey design features that influence their SPoI responses within a bounded institutional context. Seven graduate students from a College of Health Sciences at a public university participated in semi-structured online interviews. Data were analyzed thematically, resulting in four primary themes: Instructor Qualities and Classroom Interactions, Course Organization and Assessment Design, External and Peer Influences, and Student Motivation, Honesty, and Process Improvement. Findings suggest that SPoI completion is a deliberative process shaped primarily by instructional experiences, moderated by structural and contextual factors, and influenced by students’ beliefs about the utility and impact of evaluations. Implications highlight the importance of survey design refinement, midterm feedback mechanisms, and transparent institutional communication to enhance both participation and trust in evaluation systems. This study contributes context-rich insights into graduate student engagement with instructional evaluations and informs the development of a subsequent large-scale survey instrument.
Jennifer Kerzetski, Jason Zhang, Elizabeth Templeton· Florida Journal of Education...· 0 citations
First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.
Sharifuzzaman, M. Rahman· Asian Journal of Contemporar...· 0 citations
The study explores the paradigm shift in education brought about by the introduction of generative artificial intelligence (AI) tools, focusing on educational stakeholders’ self-reported perceptions rather than observed changes in teaching or learning outcomes. We consider stakeholders’ views on AI-based technologies within the teaching–learning process. The current study uses a cross-sectional empirical survey design with a sample of N = 917 respondents, including teachers, students, administrators, and management. It examines the use of advanced AI technologies such as ChatGPT, Gemini, DeepSeek, and Grok, and stakeholders’ perceived connection between digital skills and classroom performance, student motivation, and critical thinking. We also discuss the ethical dilemmas and structural challenges that accompany this digital change. Inferential statistics, such as One-Way ANOVA and the Pearson Chi-Square test, show statistically significant differences in perceptions and regulatory expectations across organizational responsibilities. The findings contribute to understanding how advanced digitalization is perceived to reshape traditional academic roles, offering practical insights for creating effective, responsible, and sustainable teaching practices.