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Research Article

Students' Perceptions of Artificial Intelligence Tools for Study Productivity and Learning

Synthesis: Oladosu (2026) surveyed 26 respondents (23 current students) on their self-reported perceptions of AI tools for studying. Most respondents viewed AI positively — 92.3% agreed it improved their understanding of subjects and 80.8% agreed it helped them complete assignments faster — yet only 38.5% said AI reduced their overall study time, and half reported sometimes or often relying on AI instead of learning independently. Open-ended responses praised explanations, summaries, and speed while flagging concerns about overreliance, reduced critical thinking, inaccurate information, and academic dishonesty. Because the sample is small and self-reported, the findings are exploratory and do not establish causal effects on learning.

The study situates itself in a fast-growing literature on students' use of ChatGPT and similar tools as on-demand learning assistants, drawing on systematic reviews (Albadarin et al., 2024; Lo et al., 2024a) that document both benefits — immediate feedback, explanations, personalized support — and risks such as overreliance and reduced critical engagement. It deliberately focuses on students' perceptions rather than objectively measured outcomes, noting that perceived improvement should not be treated as equivalent to measured academic performance. This positions the work as a small-scale complement to larger experimental syntheses (Deng et al., 2025) and as a response to calls for more attention to students' actual study habits in the AI-in-education literature.

Survey Design and Sample

The study used a cross-sectional, exploratory survey design administered anonymously online via Google Forms. The instrument contained five sections — basic information, AI usage, productivity, understanding, and perceived benefits and disadvantages — combining Likert-style closed questions with two open-ended items asking about the main benefit and main disadvantage of AI-assisted studying.

The dataset contained 26 completed responses, of which 23 respondents (88.5%) identified as current students and three (11.5%) did not. The age distribution of the full sample was 12 respondents aged 19–25 (46.2%), 12 aged 26+ (46.2%), and 2 aged 15–18 (7.7%). Participants were recruited through non-probability convenience sampling, so findings cannot be generalized beyond the sample. Analysis was purely descriptive: frequencies and percentages for closed items, with open-ended responses grouped into recurring themes, and no inferential tests. The author notes the dataset included minors, that no institutional ethics approval is claimed, and that individual-level data were not released to protect Privacy.

Reported Use and Frequency

Twenty-three respondents (88.5%) reported using AI tools for studying, while three (11.5%) did not. Reported frequency of use was daily for nine respondents (34.6%), weekly for ten (38.5%), rarely for six (23.1%), and never for one (3.8%). This indicates that AI use was widespread in the sample, with most users engaging on at least a weekly basis, consistent with broader evidence that generative AI has become routine in higher-education study practices.

The survey instrument captured which tools students used, listing ChatGPT, Google Gemini, Bing AI/Copilot, and an "other" option alongside a "do not use AI tools" category, though the manuscript's main analysis centers on aggregate frequency rather than tool-by-tool breakdowns. This frequency picture frames the perception results that follow: heavy and regular use coexists with a nuanced, partly ambivalent evaluation of the technology's effects.

Perceived Effects on Productivity and Understanding

Respondents were strongly positive about AI's effect on assignment completion. Fourteen respondents (53.8%) strongly agreed and seven (26.9%) agreed that AI helped them complete assignments faster — 21 of 26 (80.8%) agreed or strongly agreed overall. Perceived understanding was even more positive: 17 respondents (65.4%) strongly agreed and seven (26.9%) agreed that AI improved their understanding of subjects, for a combined 24 of 26 (92.3%) who agreed or strongly agreed.

Notably, these perceptions did not translate into a consensus that AI shortened total study time. Only ten respondents (38.5%) said AI reduced their study time, while thirteen (50.0%) said it did not and three (11.5%) were unsure. As the author notes, completing an individual task more quickly does not necessarily shorten the whole learning process — students may spend saved time asking more questions, exploring additional material, or verifying outputs. This distinction between task-level efficiency and overall study time is one of the study's more striking findings.

Reliance, Benefits, and Disadvantages

The perceived-benefit findings sit alongside a real tension around dependence. Thirteen respondents (50.0%) reported relying on AI sometimes or often instead of trying to learn independently (two "often," eleven "sometimes"), while six (23.1%) rarely and seven (26.9%) never did. This aligns with broader concerns in the literature about self-regulated learning and metacognitive engagement with AI, where the value of the tool depends on whether students actively evaluate outputs rather than passively accept them.

Open-ended responses framed benefits around understanding and explanation, speed and convenience, and access to information — simplifying complex material, providing explanations at an understandable level, summarizing, and offering personalized assistance. The reported disadvantages clustered around dependence or overreliance, reduced independent effort and problem-solving, reduced critical thinking, reading, focus, or memory retention, the need to verify outputs, and academic dishonesty. Several respondents reported no disadvantage or were unsure. The author stresses that the survey cannot determine whether these perceived risks actually occurred or affected academic outcomes, and that the educational value of AI likely depends on whether it supports or replaces cognitive effort — a theme tied to engagement, Motivation, and Feedback in study practice.

Limitations and Implications

The author is explicit about the study's limits: a small (N = 26) non-probability sample, self-reported rather than objective measures, a cross-sectional descriptive design that cannot establish causation, and an instrument not validated as a psychometric scale. Recommendations follow from these constraints — students should treat AI as a learning aid rather than a substitute for reasoning, verify AI outputs against reliable sources, and educators should teach evaluation and ethical use; future research should pair perceptions with objective measures (pre/post tests, grades, controlled tasks) and use larger, more diverse samples.

The paper's contribution is therefore modest but real: it documents, in a small exploratory dataset, that students can simultaneously perceive large benefits from AI and harbor serious concerns about dependence and critical thinking — a coexistence that mirrors larger reviews and suggests the effect of AI on learning depends heavily on how it is used. For the AI literacy and self-regulated learning literatures, it reinforces the need to study study habits and to move from perceived to measured outcomes.

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Citation

Oladosu, A.-A. (2026). Students' Perceptions of Artificial Intelligence Tools for Study Productivity and Learning. EdArXiv preprint.

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