2026· International journal of research and innovation in social science· Vol 10, pp. 6335-6350· 0 citations
TL;DR
The review examines four interrelated design challenges: the pedagogical quality of AI-generated multimedia; the validity and fairness of automatic feedback; the interpretability of learning analytics; and the impact on established instructional design processes.
Abstract
In higher education, multimedia authoring, feedback, analytics and adaptive learning systems are increasingly embedded with artificial intelligence (AI). The impact of these changes on instructional design for personalised learning is examined. Instead of viewing personalisation as automatic optimisation, the review examines four interrelated design challenges: the pedagogical quality of AI-generated multimedia; the validity and fairness of automatic feedback; the interpretability of learning analytics; and the impact on established instructional design processes. Research suggests that AI can broaden the diversity and responsiveness of resources for learning, but evidence of lasting learning gains is more patchy and very much dependent on human oversight, curricula and student agency. The systems may then use non-transparent models or data which are unrepresentative, thus reproducing equity concerns. The review suggests a series of principles for responsible design: pedagogical purpose; cognitive coherence; human verification; contestable personalisation; and proportionate data use. AI should be viewed as a limited design resource instead of an independent system of instruction.
It is argued that students' needs matter on their own, the field should start from what students need when deciding how to use AI in design education, and good educational frameworks should be anchored in the learner, not driven by technology.
The review proposes an AI–Pedagogy–Engagement–Critical Thinking (AI-PECT) framework in which AI capabilities influence educational outcomes through pedagogical mediation and learner agency, while AI literacy, teacher competence, infrastructure, assessment design and ethical governance operate as important contextual conditions.
Sonam Bansal and Vijay Kumar Lamba· International Journal of Adv...· 0 citations
Artificial intelligence is best understood as an amplifier of pedagogy, rather than a replacement for teachers or human judgment, and its benefits are conditional on AI literacy, transparent governance, and equitable access.
D. Bîrsan· Journal of Non-Formal and Di...· 0 citations
Artificial intelligence is being incorporated into education at a pace that exceeds the development of stable pedagogical evidence, institutional capacity, and enforceable governance. Its educational value is often framed through personalisation, rapid feedback, and efficiency, yet these affordances do not necessarily produce active learning and may instead encourage cognitive offloading, dependency, or superficial task completion. This critical narrative review integrates three bodies of scholarship that are commonly treated separately: active-learning theory and evidence, resistance to pedagogical and technological change, and ethical frameworks for artificial intelligence in education. Literature published from 1 January 2010 to 5 June 2026 was examined, with earlier foundational sources included where conceptually necessary. The synthesis indicates that artificial intelligence supports active learning most plausibly when it elicits explanation, prediction, retrieval, critique, revision, and peer dialogue rather than supplying polished answers. Meta-analyses of intelligent tutoring systems and recent experimental studies of generative artificial intelligence suggest beneficial average effects, but confidence is constrained by short interventions, locally developed assessments, weak evidence on retention and transfer, uneven disciplinary coverage, and rapid technological obsolescence. Resistance is not adequately understood as reluctance or deficient digital competence. It may signal concerns about pedagogical legitimacy, professional identity, workload, surveillance, reliability, inequity, and the erosion of human relationships. Effective mitigation therefore requires participatory design, transparent purpose, protected alternatives, assessment alignment, professional learning, and institutional support. Ethical principles are necessary but insufficient unless translated into lifecycle governance covering educational necessity, data minimisation, fairness testing, human oversight, contestability, disclosure, incident response, and periodic withdrawal decisions. An integrated framework is proposed in which pedagogical alignment, learner agency, adoption conditions, and accountable governance are treated as interdependent design requirements. Artificial intelligence can catalyse educational improvement, but only when it is subordinated to defensible learning purposes and when institutions accept responsibility for both pedagogical and social consequences.
A. Rushdi, S. S. Zagzoog, Ahmad Ali Rushdi· Asian Journal of Education a...· 0 citations
The review concludes that artificial intelligence should be treated as a socio-technical component of curriculum and assessment rather than a stand-alone productivity tool, because its educational legitimacy depends on whether institutions can preserve epistemic agency, valid judgement of learning, equitable access, and accountable human responsibility.
A. Talib· Asian Journal of Education a...· 0 citations
The paper proposes a set of guiding principles derived from the identified tensions, emphasising teacher-mediated interaction, developmental calibration of AI use, transparency, curriculum alignment, privacy protection and equity considerations, which provide a structured basis for integrating AI in ways that support learning processes while mitigating potential risks.
