Generative AI in Technology-Oriented Higher Education: A Systematized Review and Survey on Students’ Perceptions of Performance, Autonomy, and Ethical Implications
A context-bounded interpretive framework is proposed suggesting that GenAI’s educational value depends on instructional and governance conditions that preserve autonomy, critical thinking, integrity, and equity.
Abstract
Generative Artificial Intelligence (GenAI) is rapidly reshaping higher education, especially in technology-oriented programs where critical thinking and complex problem solving are core outcomes. This study triangulates global and local evidence on performance/efficiency, usage, autonomy, critical-thinking engagement, and ethics by combining a systematized review informed by Kitchenham and structured using selected PRISMA 2020 elements (2020–2025; last search: May 2025; 49 studies; Scopus, ACM Digital Library, IEEE Xplore, and SpringerLink; not prospectively registered) with an anonymous survey of 302 computing and engineering students from a single university in Ecuador. The expert-reviewed instrument showed acceptable internal consistency for most scale-based dimensions (McDonald’s ω), whereas institutional and ethics-related items were analyzed primarily at the item level. Results showed near-universal academic GenAI use (96%), with 47% of students reporting weekly use and 26% daily use. Research-related work was the most frequent application (81.5%), followed by homework (48.3%), report writing (43.7%), and exam preparation (41.7%). Although students reported perceived efficiency gains, concerns persisted about reduced analytical engagement and technological dependence (84.1%). Ethical concerns centered on dependence, authenticity, and data privacy, while institutional responses pointed to the need for formal training (96.7%) and clearer guidance. Based on this triangulation, we propose a context-bounded interpretive framework suggesting that GenAI’s educational value depends on instructional and governance conditions that preserve autonomy, critical thinking, integrity, and equity.
Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels.
C. Papaneophytou, Stella A. Nicolaou· Trends in Higher Education· 0 citations
This systematic review synthesises empirical and conceptual evidence on university pedagogical practices that cultivate critical thinking and, through it, student intellectual autonomy in digitally mediated learning environments and identifies gaps concerning long-term transfer, equity, and the assessment of autonomy in AI-saturated contexts.
Mayra Carolina Márquez López, Daniela Guardo Rua, Hugo Leonardo Gómez Hernández et al.· Journal of Intelligent Decis...· 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 explored how a higher education educator experienced and made sense of generative artificial intelligence (GenAI) within teaching, peer collaboration, and assessment practices. Using an Interpretative Phenomenological Analysis (IPA) research design, the study focused on a single participant, who is a curriculum leader and instructor at a private European university undergoing rapid shifts in GenAI policy and practice. Data were collected through a semi-structured interview and participant pre-account and analyzed using IPA’s idiographic and interpretative procedures. Findings identified three superordinate themes, including divergent peer adoption of GenAI, pedagogical adaptation and uncertainty, and assessment ambiguity. These illustrate GenAI as an ongoing disruption that reshapes collegial dynamics, teaching approaches, and conceptions of learning and authorship. While GenAI supports accessibility and content simplification, it also raises concerns about student dependency and academic integrity, prompting a shift toward evaluating students’ critical engagement with AI outputs. Interpreted through Mezirow’s transformative learning theory, GenAI emerges as a sustained disorienting dilemma, producing gradual, relational, and ongoing perspective transformation in higher education practice. Future research should extend this work beyond a single-case design to include comparative and longitudinal studies across disciplines and institutions, further examine GenAI-informed assessment models, and the extent to which existing theoretical frameworks capture the relational and evolving nature of AI-mediated educational change.
Karen K. Fujii, N. Perez· IAFOR Journal of Education· 0 citations
This study seeks to investigate the antecedents, processes, and outcomes of AI‐based education in fostering design thinking among engineering students. Through a meta‐synthesis of 151 peer‐reviewed articles published between 2015 and 2025, sourced from databases including Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect, we generated a grounded theory. Findings revealed two major antecedent domains: Students' prerequisites (including positive attitudes, basic skills, and foundational knowledge of design thinking) and Instructors' triple competencies (technological, pedagogical, and content knowledge). These factors collectively shape students' intention to learn design thinking (DT), represented by the core themes of usefulness and success, and moderated by variables such as demographics and ethical awareness. The instructional process was structured around the five core stages of the design thinking framework—Empathy, Definition, Ideation, Prototyping, and Testing—with relevant AI tools integrated into each stage to facilitate activities, yielding 46 open codes. The outcomes were categorized into two themes: the development of design thinking competencies and social‐psychological growth. The findings highlight AI's significant contribution to fostering both technical skills and ethical reasoning, offering an instructional model that can serve as a practical guide for enhancing design thinking skills among engineering students.
Mehdi Mohammadi, Farzane Deimehkar Haghighi· Computer Applications in Eng...· 0 citations
Purpose of the study: This study explored how 7E-instructed lessons on electronics concepts improved 21st-century skills among Senior High School (SHS) physics students in the Birim North District, Eastern Region, Ghana. Driven by Constructivist Learning Theory, Experiential Learning Theory, and the P21 Framework for 21st-Century Skills, it focused on three questions: students’ proficiency in critical thinking, creativity and innovation, and problem-solving; the impact of 7E lessons on these skills; and the appearance of these skills in students’ post-test responses.
Methodology: Using a pragmatist approach, a mixed-methods design with concurrent triangulation was employed, combining a pre-test-post-test quantitative assessment with qualitative content analysis. The study involved 103 SHS 2 physics students from two purposively chosen public schools.
Main Findings: The main assessment, a 21st-Century Skills Test on Electronics Concepts, was scored with detailed, task-specific rubrics. Pre-test results indicated that all three skills were at low levels, with average scores below 2.0 on a 4-point scale, highlighting the shortcomings of traditional, teacher-centered teaching. After six weeks of the 7E instructional program, paired-samples t-tests revealed significant improvements across all skills, with large effect sizes: critical thinking (d = 0.912), problem-solving (d = 0.867), and creativity and innovation (d = 0.834). Qualitative analysis uncovered three themes in post-test responses: enhanced analytical integration, the emergence of feasible novel ideas, and structured procedural coherence.
Novelty/Originality of this study: The 7E Instructional Model may be an effective, curriculum-aligned strategy for fostering 21st-century skills among Ghanaian SHS physics students in electronics.