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Cited by 81Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance

Open access
Ward, Ben, Bhati, Deepshikha, Neha, Fnu, Guercio, Angela·2025·2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)
DOI 10.1109/ccwc62904.2025.10903692
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Abstract

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits.

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12 of 12 references verified0 citation errors · 0 not checkable

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  • Underperforming students may utilize AI for superficial support or content replication.

    “Conversely, underperforming students may utilize AI for superficial support or content replication.(Ward et al., 2025)”

    From <b>Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education</b> (2026) · cites this paper as Ward et al., 2025

    The General Feedback on AI in Education section (page unavailable) says students use AI to fill out study guides and cautions against over-reliance, which lends some support to the possibility of superficial use. It does not identify underperforming students as more likely to use AI this way or report content replication. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • High-achieving students (better CGPA) utilize AI tools as supplementary resources rather than replacements for learning.

    “Evidence indicates that high-achieving students (better CGPA) utilize AI tools as supplementary resources rather than replacements for learning.”

    From <b>Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education</b> (2026) · cites this paper as Ward et al., 2025

    The General Feedback on AI in Education section says several students viewed AI as a supplementary tool, and the Comparative Studies section describes AI tools as complements rather than replacements. Neither passage links that approach to high-achieving students or better CGPA; the Grade Level Distribution section concerns year of study, not academic achievement. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 2

  1. G. Elhussein, E. Hasselaar, and O. Lutsyshyn, "Shaping the future of learning: The role of ai in education 4.0," in Proceedings of the World Economic Forum Annual Meeting. World Economic Forum, 2024.
  2. R. Baker, "AI in education: Promises and implications for teaching and learning," Technology-Based Assessment for 21st Century Skills: Theoretical and Practical Implications from Modern Research, pp. 253- 268, 2019.

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Authors
Ward, B., Bhati, D., Neha, F., & Guercio, A.
Affiliations
Kent State University (US)
ORCID
Venue
2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)
Publisher
IEEE
Published
2025-01-06
Pages
00434-00440
DOI
10.1109/ccwc62904.2025.10903692
OpenAlex
W4409278300
Cited by
48
References
12
Citations by year
2026: 25 · 2025: 23
FWCI
54.89
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Annual Computing and Communication Workshop and Conference (CCWC) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
AI tools in education, study habits, academic performance, personalized learning, adaptive testing, time management, student feedback, overreliance on AI, student perceptions of AI

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