Oct 2026· International journal of technology in education and science· 0 citations
TL;DR
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.
Abstract
The rapid rise of artificial intelligence is reshaping higher education, yet its role in engineering education remains fragmented and unevenly understood. This systematic review synthesized sixty-nine peer-reviewed studies published between 2015 and 2024 to examine how artificial intelligence tools are being integrated into engineering curricula. Guided by a technology–pedagogy–ethics framework, the analysis explored the types of artificial intelligence tools employed, their pedagogical applications, and the ethical concerns associated with their use. The findings indicate 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. By contrast, applications supporting collaborative learning, industry-linked design projects, and ethical training remain limited. Generative artificial intelligence emerged as both the most rapidly adopted and the most ethically contested technology, raising questions about academic integrity and originality. This review contributes by offering a discipline-specific synthesis that highlights the pedagogical opportunities and risks of artificial intelligence in engineering education and calls for more deliberate alignment of technological innovation with educational practice and ethical responsibility.
Artificial Intelligence (AI) is increasingly reshaping curriculum development in higher education as a
socio-technical, multidimensional phenomenon rather than a neutral technological tool. This study explores how AI
influences ethical governance, pedagogical design, institutional capacity, and contextual dynamics within curriculum
systems. Despite growing scholarly attention, existing literature remains fragmented, with ethical, pedagogical, and
institutional dimensions often examined in isolation, limiting a comprehensive understanding of their interdependencies.
Using a narrative integrative review design, this study synthesizes findings from 55 peer-reviewed studies
retrieved from major academic databases, including Scopus, Web of Science, ERIC, IEEE Xplore, and ScienceDirect.
The analysis employed systematic coding and thematic synthesis across ethical-policy, pedagogical-design,
and technical-institutional domains. Findings reveal that AI introduces systemic ethical risks, including algorithmic
bias, transparency deficits, and data governance challenges, while simultaneously transforming pedagogical
practices through personalization and adaptive learning. However, these advances also raise concerns regarding
epistemic narrowing and the redistribution of human agency in teaching and learning processes. At the institutional
level, AI implementation is constrained by infrastructure limitations, governance misalignment, and dependence on
external platforms. Contextual factors further demonstrate that AI curriculum implementation is highly cultureand
institution-specific, challenging the feasibility of universal models of adoption. The study identifies a persistent
fragmentation in existing research and proposes the Integrated Challenge Model for AI Curriculum Development
(ICM-AI-CD), which conceptualizes AI integration as a dynamic socio-technical system comprising interdependent
ethical-policy, pedagogical-design, and technical-institutional domains. The study concludes that AI in curriculum
development represents a systemic transformation of higher education, requiring integrated frameworks that capture
its cascading and interdependent effects. This framework provides a foundation for future research, policy development,
and institutional planning in AI-enabled curriculum systems.
Abdul Malik· Applied Business: Issues &am...· 0 citations
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
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. López-Vargas, Javier Rodríguez-Vidal, Ángel García-Beltrán et al.· Education and Information Te...· 0 citations
The aim of this study was to analyze the opportunities, risks, and pedagogical transformations associated with the use of artificial intelligence in higher education learning processes. A theoretical review based on documentary analysis was conducted through the critical examination and comparative synthesis of scientific literature published between 2021 and 2026 in databases such as Scopus, Web of Science, and ERIC. The analysis explored the main dimensions through which artificial intelligence is reshaping university teaching and learning, including personalized learning systems, generative AI tools, algorithmic assessment, and emerging ethical and epistemological challenges. The reviewed literature revealed that artificial intelligence offers significant benefits depending on institutional contexts and the pedagogical approaches guiding its implementation, while also posing risks related to academic dependency, superficial learning, and assessment bias. The opportunities and risks identified represent an interdependent tension that influences the effective integration of artificial intelligence into higher education. The findings suggest that the pedagogical transformation enabled by artificial intelligence depends less on the sophistication of the technologies themselves and more on the institutional capacity to establish curricular guidelines, implement ethical policies, and foster critical digital competencies among both teachers and students.
Félix Antonio Díaz, Juan Carlos Toribio Fernández, J. Martínez-Alonzo et al.· MENTOR revista de investigac...· 0 citations
The increasing use of Generative Artificial Intelligence in Higher Education has created new challenges for academic integrity, intellectual authorship, and the development of critical thinking. This study analyzes these challenges and evaluates a pedagogical intervention designed to promote the responsible use of Generative Artificial Intelligence in graduate education. The study was conducted between February 2025 and June 2026 and involved a total of 50 graduate students from three cohorts enrolled in two Master’s programs in Engineering and Business Administration. A qualitative educational action research approach was adopted, based on the analysis of academic assignments, similarity reports, and Artificial Intelligence-assisted writing detection using Turnitin, together with classroom observations and reflective discussions. The pedagogical intervention incorporated strategies based on the UNESCO (2023) guidance, structured prompt design, and the Socratic model. The findings revealed frequent use of Generative Artificial Intelligence-generated content without adequately paraphrasing the generated material, verifying information, or consulting the scientific literature. Following the intervention, students demonstrated greater attention to question formulation, information validation, and the use of reliable academic sources. The study contributes empirical evidence 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· 0 citations
Generative Artificial Intelligence (GenAI) has rapidly transformed higher education practices, creating new opportunities for pedagogical innovation while introducing complex challenges related to assessment validity, academic integrity, and institutional governance. However, existing studies remain fragmented across technological adoption, learning processes, assessment practices, and ethical considerations, limiting a comprehensive understanding of how GenAI can be integrated responsibly into higher education ecosystems. This systematic literature review aims to synthesize current evidence on the educational implications of GenAI by examining its influence on teaching transformation, student learning, assessment redesign, and academic integrity governance. Following the PRISMA framework, relevant studies were systematically identified, screened, and analyzed to reveal emerging patterns, challenges, and future research directions in GenAI adoption within higher education. The synthesis revealed four interconnected themes: (1) transformation of teaching practices through AI-supported instructional design and efficiency improvement, (2) enhancement of student learning through personalization and self-regulated learning support, (3) evolution of assessment toward authentic and competency-oriented approaches, and (4) development of institutional governance frameworks addressing ethical, privacy, transparency, and integrity concerns. The findings indicate that successful GenAI integration requires a balanced approach combining technological capability, pedagogical redesign, and responsible governance. This review contributes to Artificial Intelligence in Education (AIED) research by proposing an integrated perspective for sustainable GenAI adoption and identifying priorities for future empirical investigations.
Syusinka Rahmatika, Martanto, Ryan Hamonangan· Immortalis Journal of Interd...· 0 citations