Learning or Just Finishing Faster? The AI Learning Paradox in Management Education: Student Perspectives on Academic Efficiency, Critical Thinking, Cognitive Independence and Experiential Learning
DOI:
https://doi.org/10.65222/VIRAL.2026.9.50.70Keywords:
Abstract
The rapid integration of generative artificial intelligence (AI) and emerging AI agents into higher education is transforming students' learning practices, academic productivity, and approaches to complex problem-solving. However, an important pedagogical question remains: does AI primarily accelerate academic task completion, or does it contribute to deeper understanding and the development of independent intellectual competencies? This study investigates the relationship between AI-assisted academic efficiency and perceived cognitive development, drawing on a questionnaire-based investigation involving 93 management students. The research employed a structured questionnaire comprising 15 questions addressing AI usage patterns, perceived time savings, conceptual understanding, problem-solving strategies, critical-thinking abilities, cognitive independence, and expectations regarding human-centred educational practices.
The reported response distributions indicate that 78.0% of students use AI several times per week or more frequently, while 67.5% estimate time savings of at least 10% when completing academic assignments. Although 74.0% consider AI very or extremely helpful for understanding difficult concepts, only 38.2% report improvements in independent critical thinking, whereas 34.1% perceive a reduction in their cognitive independence. These findings suggest a potential discrepancy between perceived academic productivity and cognitive development, indicating that faster task completion does not necessarily translate into deeper learning. The analysis further highlights the importance of responsible AI use, critical verification of generated information, and meaningful human interaction in technology-enhanced educational environments.
Building on these findings, the study introduces the FAST–DEEP conceptual model, distinguishing productivity-oriented AI engagement from learning-oriented practices that promote reflective reasoning, intellectual autonomy, and practical knowledge application. The article advocates integrating AI-supported business projects, reflective problem-solving activities, and human-centred assessment strategies into management curricula. Its principal contribution lies in demonstrating the importance of evaluating AI-assisted education beyond immediate efficiency gains and reconsidering pedagogical approaches that prepare students to collaborate effectively with intelligent technologies while maintaining independent judgement and responsibility.
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