Aug 2026· Education and Information Technologies : Official Journal of the IFIP technical committee on Education· 0 citations· 22 references
TL;DR
The preliminary findings suggest that Generative artificial intelligence use in engineering education requires attention not only to tool access or technical performance, but also to students’ interaction strategies, verification practices, and ethical evaluation of context-specific uses.
Abstract
Generative artificial intelligence is increasingly incorporated into engineering education, particularly in programming courses, raising questions about its effects on learning processes and its acceptability in academic contexts. Empirical evidence examining both interaction patterns and ethical evaluation of Generative artificial intelligence use in real classroom settings remains limited, and studies addressing these dimensions jointly are scarce. This study adopts a multi-phase empirical design to examine student engagement with Generative artificial intelligence across these two complementary dimensions. In Phase 1, a classroom-based activity was conducted with 340 first-year engineering students who completed a time-constrained debugging task using Generative artificial intelligence as the sole external assistance tool. The results indicate that access to Generative artificial intelligence did not ensure reported successful task completion. Instead, successful outcomes were associated with prompt efficiency and verification practices, whereas higher prompting frequency was negatively associated with task success under time constraints. In Phase 2, a qualitative exploratory study was conducted with 16 engineering students through a structured role-play activity simulating an ethics committee, followed by individual voting and survey-based data collection. Acceptance was higher when Generative artificial intelligence was framed as supportive or formative, and lower in scenarios involving summative assessment, surveillance, or autonomous decision making. These preliminary findings suggest that Generative artificial intelligence use in engineering education requires attention not only to tool access or technical performance, but also to students’ interaction strategies, verification practices, and ethical evaluation of context-specific uses. From a socio-technical perspective, the study advances engineering education research by linking technical interaction with GenAI to verification, self-regulation, and ethical responsibility. This highlights the need for pedagogical approaches that integrate technical guidance and ethical reflection into engineering curricula.
A systematic review of peer-reviewed studies published between 2015 and 2024 concludes that while tools such as generative artificial intelligence, intelligent tutoring systems, virtual and augmented reality, and predictive analytics are increasingly present, their adoption is concentrated in specific areas, particularly problem-solving, simulation, and assessment.
Firas Almasri· International journal of tec...· 0 citations
The rapid institutionalization of generative artificial intelligence (GenAI) in higher education has created an urgent need for empirical evidence on how structured course-level integration relates to student engagement and perceptions of learning. This study examines a usefulness-centered conceptual framework combining elements of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) with the digital competence perspective of DigComp 2.2. A structured pedagogical pilot intervention requiring all students to use generative AI tools was implemented in an undergraduate Public Service Management course (n = 76). Students completed AI-supported group assignments and an immediate post-intervention questionnaire comprising 19 Likert-scale items, four demographic questions, and four optional open-ended questions that are not analyzed in the present paper. Because most variables were non-normally distributed, non-parametric statistical methods were applied, including Spearman’s rank correlations, Mann–Whitney U tests, and Kruskal–Wallis tests. Perceived learning usefulness was strongly and positively associated with both frequency of AI use and satisfaction with the learning process. Ethical attitudes were also positive, but more weakly associated with frequency of use. Demographic group differences were observed mainly in specific usage patterns rather than in general attitudes towards AI-supported learning. These exploratory findings suggest that perceived learning usefulness remains relevant in mandatory AI-integration contexts. Pedagogical scaffolding—including prompt literacy, verification practices, and reflective documentation—provides a structured framework for guided and responsible use of generative AI tools in higher education.
Emese Belényesi, M. Korpics, Tamás Méhes et al.· Trends in Higher Education· 0 citations
This study investigates how pre-service teachers experience generative artificial intelligence tools in their learning processes and how they plan to integrate these technologies into their future teaching practices. The study employed a qualitative phenomenological design and was conducted with 28 pre-service teachers enrolled in the English language teaching program of a public university in Türkiye. Semi-structured online interviews were carried out, and the data were analyzed through content analysis. Coding was conducted independently by two coders, achieving a high level of inter-rater agreement. The findings reveal that pre-service teachers perceive generative artificial intelligence as a supportive learning partner in areas such as time management, idea development, linguistic accuracy, and self-regulation. However, they also highlighted risks such as over-reliance, diminished creativity, ethical concerns, and the generation of inaccurate information. The participants emphasized that while generative artificial intelligence may serve as a complementary tool in learning processes, classroom integration requires transparency, critical verification, and active teacher guidance as essential conditions. The originality of the study lies in its holistic exploration of pre-service teachers’ experiences with generative artificial intelligence across pedagogical, ethical, and professional identity dimensions. These findings provide qualitative evidence for the relatively underexplored perspectives of pre-service teachers, thereby offering a new lens for ongoing debates on the role of generative artificial intelligence in teacher education.
Mehmet Kokoç· Uludağ Üniversitesi Eğitim F...· 0 citations
Empirical evidence is contributed from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
Rodrigo Florencio da Silva· Information· 1 citation
A ten-step teaching framework for AI-supported creative interactive content design is proposed, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship.
Belén Mainer, Ana Pérez-Escoda· Education sciences· 0 citations
This study examined university students’ use of generative artificial intelligence (AI) in relation to engagement, confidence, ethical awareness, and perceived academic value. A descriptive-quantitative, cross-sectional design was employed, involving 380 students from 11 colleges of a Philippine state university. Data were collected using a structured questionnaire and analyzed using descriptive statistics. Students reported using AI-assisted applications primarily for research, summarization, writing, and editing, with ChatGPT, QuillBot, and Grammarly among the most frequently reported tools. Confidence in independent AI use was concentrated in the moderate and lower categories, while perceptions of AI-supported engagement were neutral overall. Ethical awareness was uneven: students showed stronger recognition of familiar concerns involving copyright and unethical academic use but greater uncertainty regarding the broader meaning of ethical AI use in education. Taken together, the findings indicate that access to and use of AI tools do not necessarily develop alongside confidence, meaningful engagement, and ethical understanding. The study highlights the need for AI literacy initiatives that combine practical skills with critical evaluation, appropriate disclosure, verification of generated information, and student accountability in AI-assisted academic work.
Daisy A. Mamaril, Lailani C. Banggawan· Journal of Intelligent Decis...· 0 citations