ISSN 2979-8582 · Article No. 079
Aruwa Benedict Mohammed: Department of Educational Psychology, Bingham University, Karu, Nasarawa State, Nigeria
Favour Mosunmola Sobowale: Department of Educational Technology, School of Science and Technology Education, Federal University of Technology, Minna, Nigeria
Samuel King Ejeh: Innovation Management and Policy, Institute for Statistical Studies and Economics of Knowledge. National Research University – Higher School of Economics Moscow, Russia
Adaptive educational technologies increasingly decide, on a learner's behalf, when a session should end. Exposure governors, session caps, fatigue detectors and well-being modules are presented as protective, and over short horizons they frequently are. This conceptual and methodological paper argues that they carry a developmental cost that no current evaluation framework detects. A child whose stopping is always performed by software does not practise stopping, yet the capacity to disengage from an absorbing digital task is itself a developmental achievement that matures through exercised judgement. I name this capacity self-withdrawal capability and define it as a three-component construct comprising noticing (interoceptive and metacognitive detection of diminishing return), appraisal (judging that this is the point to stop), and enactment (acting on that judgement against competing motivational pull). The construct is distinguished from self-regulated learning, trait self-control, and digital self-control tools research. I then formalise what I call the regulator's paradox: protective withdrawal architectures may suppress the very competence whose absence justifies them, so that an intervention evaluated only on within-treatment exposure will appear most successful at exactly the moment it is doing the most long-term damage. The paper's principal contribution is a measurement architecture assembled largely from data that adaptive systems already collect, including the ratio of voluntary to enforced terminations, calibration error between learner-predicted and analytically derived stopping points, override rates against self-set limits, and a governance-disabled fade probe. It closes with a design principle, transferable withdrawal authority, under which stopping rights migrate from system to learner on a developmental schedule, and with the evaluative claim that a humane learning system should be judged on whether learners eventually stop needing it.
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British Journal of Contemporary Research
Open Access · Peer Reviewed · Published by Bexford Publishing Ltd
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