Document Type : Original Research Paper-English Issue
Author
Department of Educational Administration, Farhangian University, Tehran, Iran
Abstract
Background and Objectives: The rapid advancement of AI (AI) in higher education, particularly through adaptive learning systems, intelligent tutoring agents, and generative AI tools, has catalyzed a paradigm shift in traditional teaching and learning models. Also, the increased use of AI in higher education in many different fields offers a more personalized, responsive, and effective learning experience. Yet, as AI systems increasingly determine how students interact with and process knowledge, and create knowledge outputs, it is important to answer both basic questions about learner agency in relation to the very paradigm of the autonomous learner that underpins theories of adult and higher education. This article considers whether AI-enhanced learning environments support or deter learners’ sense of cognitive authorship.
Methods: This study used a qualitative method that is a mixture of conceptual and empirical inquiry to show pressing issues related to educational theory and emerging technologies: in the age of AI, are self-directed learners still authors of their learning? The philosophical method examines issues about the ontological and epistemic grounds of cognitive agency in an AI-mediated context using semi-structured interviews with 23 students in different fields at Tehran universities. It includes the question of the lived experience of university students who are engaging with AI to be educated. To increase methodological precision Colaizzi‘s seven step was used; this was chosen because of its specificity and effective depth for interpretation, as well as its fit with a focus on phenomenology in its aim to examine how 'agency', 'authorship', and 'self-direction' of students are lived in an algorithmic context that is mediating cognitive agency. This method includes seven steps: 1) Reading and re-reading all the transcribed interviews to make sense of them, 2) Extracting significant statements. These are phrases or sentences that directly pertain to the investigated phenomenon, 3) Giving meaning to the statements: During the process pertinent quotes are categorized the themes are generated based on multiple statements that convey similar meaning, 4) Repeating step 1-3 for each interview, then the researcher can begin to create theme based on the formulated meaning, 5) Gathering an exhaustive description of everything generated in step 1-4, 6) Summarizing the exhaustive description so that there is an identification of the fundamental structure of the phenomenon and 7) Ensuring the credibility of the data through member checking.
Findings: Results indicated that using AI in different fields has both negative and positive effects on learning and cognitive agency of students. Central outcomes on the positive side include cognitive scaffolding, increased access to complex knowledge, the ability of AI as a cognitive extension, co-authorship with AI through critical engagement, increasing critical thinking, and improving self-learning through simulation and interactive environments. On the other hand, it has some challenges such as: Ambiguous intellectual authorship, Erosion of epistemic self-Trust, Illusion of mastery, Passive deference to algorithmic authority, and ethical dissonance and academic guilt.
Conclusions: Based on these findings and different aspects of AI in student cognitive agency, the paper proposes a framework for self-directed learning in the age of AI in which authorship is not isolated, as the learner is still the one who makes learning; however, this issue needs basic changes in pedagogical, curricular, and philosophical perspectives.
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© 2026 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)
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