Cognitive Miser Theory as a Lens for Rethinking Cognitive Effort During Difficult and Complex Academic Tasks in AI-Supported Self-Regulated Learning

Abstract

Artificial intelligence (AI) offers new opportunities to support individual goal setting and self-regulated learning (SRL). However, its influence on learners’ cognitive effort remains a critical issue. While AI-supported environments can scaffold difficult and complex academic tasks, they may also encourage learners to rely on intuitive, automatic, or heuristic problem-solving strategies rather than sustained analytical engagement. This case study examines how doctoral students in educational technology and distance education in Türkiye use AI during difficult and complex academic tasks through the lens of Cognitive Miser Theory (CMT). Using semi-structured interviews with seven frequent AI users and inductive thematic analysis, the authors identify four interrelated themes: AI’s supportive role in decision-making, effects on cognitive processes, adaptive influence on habit formation, and transformative impact on strategic study approaches. Results indicate that students frequently rely on AI to regulate time, stress, and workload, thereby reducing perceived cognitive effort. However, students with higher metacognitive awareness engage with AI more critically, which, paradoxically, increases their perceived cognitive load. In contrast, under conditions of uncertainty, unfamiliarity, or time pressure, participants tend to rely on AI more uncritically, raising concerns regarding academic depth, learner autonomy, emotional well-being, and epistemic responsibility. The study concludes that AI use in SRL functions as a double-edged cognitive tool: it can either mediate cognitive efficiency or foster cognitive complacency, depending on learners’ strategic orientations and metacognitive capacities. The findings underscore the critical importance of strengthening AI literacy, metacognitive regulation, and calibrated cognitive load management in advanced academic learning contexts. Future studies should incorporate one-on-one sessions with participants, using think-aloud protocols, to achieve a deeper understanding of the topic under examination.