Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]12/09/2025 at 2:10 PM[/if 2224] | [if 1553 equals=””] Volume : 16 [else] Volume : 16[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 33 38

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    Rupali A. Vinchurkar, Gajendra R. Bamnote,

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  1. Assistant Professor, Principal, PG Department of Computer Application, Prof. Ram Meghe Institute of Technology and Research, Badnera, Professor, Department of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology and Research, Badnera, Maharashtra, Maharashtra, India, India

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Abstract

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nThe proliferation of artificial intelligence (AI) and machine learning (ML) technologies has transformed the digital education landscape by enabling adaptive e-learning systems capable of personalizing content and optimizing learning paths. This study provides a systematic analysis of adaptive e-learning algorithms within the framework of heutagogy, an educational paradigm that emphasizes learner autonomy, self-direction, and capability development. The convergence of adaptive technologies with heutagogical principles offers new avenues for creating more engaging and personalized learning environments. A critical review is conducted on three prominent algorithmic approaches: Bayesian Knowledge Tracing (BKT), Deep Knowledge Tracing (DKT), and Reinforcement Learning (RL). These models are evaluated based on their capacity to track learner progress, predict future performance, and adjust instructional strategies in real time. BKT models student knowledge probabilistically, DKT applies deep learning via recurrent neural networks to model learning sequences, and RL uses policy optimization to maximize long-term learning outcomes through feedback mechanisms. Empirical evidence from recent studies suggests that these AI-powered systems significantly enhance learner engagement, retention, and academic performance. However, challenges such as data privacy, algorithmic fairness, model interpretability, and ethical deployment remain central to future development. The study discusses the role of explainable AI (XAI) in addressing these challenges and proposes directions for integrating adaptive systems more ethically and effectively into heutagogical learning environments.nn

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Keywords: Adaptive learning, heutagogy, artificial intelligence in education, self-directed learning, deep knowledge tracing (DKT)

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications ]

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How to cite this article:
nRupali A. Vinchurkar, Gajendra R. Bamnote. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis[/if 2584]. Journal of Computer Technology & Applications. 07/08/2025; 16(03):33-38.

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How to cite this URL:
nRupali A. Vinchurkar, Gajendra R. Bamnote. [if 2584 equals=”][226 striphtml=1][else]Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis[/if 2584]. Journal of Computer Technology & Applications. 07/08/2025; 16(03):33-38. Available from: https://journals.stmjournals.com/jocta/article=07/08/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 16
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 12/05/2025
Accepted 21/07/2025
Published 07/08/2025
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Publication Time 87 Days

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