The AI Study Habit Loop: Linking Tech Use, Time Management, and Self-Regulation Among Northwestern University Students

Eric  S. Parilla,

The AI Study Habit Loop: Linking Tech Use, Time Management, and Self-Regulation Among Northwestern University Students

Abstract:

This study assesses how far artificial intelligence (AI) is used by Northwestern University students and how AI affects their time management capacities, with self-regulation as a mediating factor. The study is grounded in Zimmerman’s Self-Regulated Learning (SRL) Theory, with a backdrop of philosophical reflections that can be traced to Wojty?a on human moral action. It adopts a quantitative- correlational design involving 488 students from different colleges. Data were gathered through a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and mediation analysis on Jamovi. Results showed that students make average use of AI, with a mean of 2.75, especially in terms of content development and grammar checks, but did not use this as part of their daily study habits. While students generally showed high self-regulatory behavior in goal setting and reflection ( ?? = 3.01), they scored lower in motivation and discipline. Perceptions of time management have an overall positive response. It has a mean of ?? = 2.98 with noted strengths in task prioritization and completion, but with weaknesses in scheduling and pacing. The mediation analysis then proved that self-regulation significantly mediates the relationship between the use of AI and time management, signaling that internal cognitive strategies enhance the effectiveness of AI. This study concludes that although AI tools offer great support while learning, their potency can only be attained when the students embrace self-discipline and reflective engagement. Ethical responsibility and intentional modes of learning will not be substituted by technology. This result implies that institutions ought to pair AI literacy with self-regulated learning programs. Longitudinal and qualitative approaches may also help future studies understand more of such patterns in behavior alongside moral considerations of learning with AI.

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