Nov 2024· Academia AI and Applications· Vol abs/2411.00631· 23 citations· 90 references
Computer Science
TL;DR
The findings suggest that while GenAI may enhance learner agency through personalisation and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts.
Abstract
Conversations regarding the societal implications of artificial intelligence (AI) and its integration into educational processes are ongoing. An area of great significance in this regard is agency, including how the use of AI technologies impacts an individual’s ability to exercise autonomy. In educational contexts, this remains a relatively unexplored yet vital subject. To shed light on this subject, this paper provides an exploratory thematic review of works which investigate the relationship between generative artificial intelligence (GenAI) and agency in education, mapping and interpreting the available literature through the lens of critical digital pedagogy (CDP). The findings suggest that while GenAI may enhance learner agency through personalisation and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review also contributes to new sociotechnical perspectives on AI, highlighting how GenAI technologies may shape and be shaped by power dynamics and pedagogical ideologies. This review contributes to the existing debate on how AI technologies can be deployed in education and society responsibly by exposing how the use of these technologies may lead to unintended impacts, including the entrenchment of inequality and the reshaping of concepts of individual agency, authorship, and autonomy.
With the rapid development of artificial intelligence (AI), generative artificial intelligence (GenAI) has been widely applied in higher education, specifically bringing both opportunities and potential challenges. This review focuses on the application of GenAI in teaching, learning, assessment and institutional governance within the higher education context. By adopting a literature review approach, this paper reviews and analyses research on GenAI in education. The findings reveal that GenAI can effectively improve teaching efficacy, enable personalised learning experiences, and streamline assessment procedures. However, its implementation also draws attention to concerns regarding academic integrity, data privacy, algorithmic bias, and ethical governance. In accordance, higher education institutions should strengthen AI literacy training for faculty and students, while improving institutional guidelines and establishing responsible governance mechanisms. Future research is recommended to conduct longitudinal investigations, cross-cultural comparative analyses, and in-depth studies on teacher professional development, in order to support the sustainable and long-term integration of GenAI into higher education.
Xi Bi· Exploring Science Academic C...· 0 citations
This study explored the emerging trends, current practices, challenges, and pedagogical implications of Artificial Intelligence in Education (AIEd) in the Division of Oroquieta City during the School Year 2025- 2026. Employing a descriptive qualitative research design, data were gathered from purposively selected public secondary school teachers through an adapted questionnaire, interviews, and document analysis. The findings revealed that AI is increasingly used as a teaching and learning support tool for lesson planning, assessment, student engagement, and administrative tasks, contributing to improved efficiency and personalized learning. However, challenges such as limited infrastructure, unequal access to technology, capacity and skills gaps, ethical and academic integrity concerns, and resistance to AI adoption persist. The study further indicated that AI integration transforms the teacher's role toward facilitation and mentoring while highlighting the need to strengthen learner-centered approaches, critical thinking, and digital literacy. Overall, the study underscores the importance of institutional readiness, professional development, and clear policy frameworks to ensure ethical, equitable, and sustainable AI integration in education.
Maria Benjie Ann M. Cabasag· Journal of Educational Resea...· 0 citations
This study presents an abductive analysis of interview data from 13 Finnish teachers involved in national AI education development projects and conceptualises a potential synergy between the domains where students' self‐determined, informed engagement with AI is placed at the centre of educational efforts in AI education.
Janne Fagerlund, Pekka Mertala, Jukka Lehtoranta et al.· Journal of Computer Assisted...· 0 citations
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
Generative artificial intelligence (GenAI) has been primarily framed as an impartial educational tool. However, this framing overlooks an even larger shift: the reassignment of epistemological authority from teachers to students to machines. This paper presents a conceptual evaluation of the extent to which GenAI redistributes students'and teachers'ability to act in classrooms to produce knowledge, validate each other's claims, and create evidence of student learning while collaborating with and competing against humans. This evaluation draws on various theoretical paradigms, including Distributed Agency, Self-Determination Theory, Society 5.0, and Technology Integration Paradigms, including TPACK and SAMR. While all of the theoretical paradigms evaluated are relevant to the role of agency within education mediated by AI, none of them address the ongoing disparity regarding equitable distribution of power, ownership of the data used to mediate interaction, and accountability in relation to human-mediated interactions. As such, this paper introduces the Ecological Co-Agency Framework, which defines agency in terms of relational, regulatory, and pedagogical processes, conditioned by a defined commitment to human accountability for epistemological claims.
Architectural education is being reshaped as Artificial Intelligence (AI) challenges human-centered conceptions of creativity, authorship, and knowledge production. However, current discussions on AI-supported architectural education remain largely focused on tool adoption, productivity, creativity, and student perception, while the policy implications of AI for curriculum design, studio governance, assessment, educator training, and ethical accountability remain underdeveloped. Addressing this gap, the study develops a policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles. The study adopts a two-stage review design that combines a conceptual framing review of posthuman pedagogy with a systematic synthesis of empirical and pedagogical studies on AI, computational design, and architectural education published between 2010 and 2025. The review identifies three interrelated dimensions of AI-supported posthuman learning: distributed agency, in which design intelligence is shared across students, educators, AI systems, datasets, interfaces, materials, and studio environments; situated knowing, in which AI becomes pedagogically meaningful only when embedded in reflective, material, and context-sensitive design inquiry; and ethical entanglement, in which authorship, bias, accountability, originality, dependency, and environmental responsibility become core educational concerns. Based on these findings, the paper proposes ecological intelligence as a design education policy principle: the capacity to think, design, evaluate, and act responsibly within interconnected human, technological, material, environmental, and institutional systems. The contribution of the study is twofold. First, it clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education. Second, it translates this theoretical perspective into a policy-oriented pedagogical framework that can inform curriculum development, studio pedagogy, assessment criteria, and ethical governance in architectural education.
