Unlocking Learning Potentials: The Transformative Effect of Generative AI in Education Across Grade Levels
Pith reviewed 2026-05-23 00:14 UTC · model grok-4.3
The pith
Generative AI most strongly shapes students' ideas about appropriate use while showing the weakest effects on learning interest and self-confidence, with larger reported benefits for college students than high schoolers.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Through a hybrid-survey method, the study finds that among the six LIPSAL areas, generative AI exerts the greatest impact on the concept of appropriate use and the lowest impact on learning interest and self-confidence. Grade comparisons reveal variation in high and low factors, with college students exhibiting higher levels than high school students across all LIPSAL areas. Interviews show students hold comprehensive understanding, positive attitudes, and strong willingness to use generative AI, with prospects and challenges noted and expectations of greater future impact as the technology matures.
What carries the argument
The LIPSAL framework, which measures generative AI effects across six student learning areas through combined questionnaire and interview data.
If this is right
- Generative AI will exert greater effects on students as the technology matures.
- Students across grades hold positive attitudes and high willingness to use generative AI.
- The reported impact varies by grade, with college students higher than high school students in all six areas.
- The findings can clarify differences in usage by students at different levels and inform digital education research.
Where Pith is reading between the lines
- Curricula could focus on appropriate use training to align with the area where generative AI shows its strongest reported influence.
- The grade-level gap suggests designing supports for high school students to help them gain similar reported benefits as college students.
- Positive student attitudes may partly reflect broad excitement about new tools rather than measured outcomes from generative AI specifically.
Load-bearing premise
The hybrid-survey method of questionnaires plus interviews produces reliable measures of generative AI impact without external validation, control groups, or checks for self-report bias.
What would settle it
A controlled study comparing students who use generative AI against those who do not, using objective measures of learning interest, self-confidence, and appropriate use rather than self-reports.
read the original abstract
The advent of generative artificial intelligence (GAI) has brought about a notable surge in the field of education. The use of GAI to support learning is becoming increasingly prevalent among students. However, the manner and extent of its utilisation vary considerably from one individual to another. And researches about student's utilisation and perceptions of GAI remains relatively scarce. To gain insight into the issue, this paper proposed a hybrid-survey method to examine the impact of GAI on students across four different grades in six key areas (LIPSAL): learning interest, independent learning, problem solving, self-confidence, appropriate use, and learning enjoyment. Firstly, through questionnaire, we found that among LIPSAL, GAI has the greatest impact on the concept of appropriate use, the lowest level of learning interest and self-confidence. Secondly, a comparison of four grades revealed that the high and low factors of LIPSAL exhibited grade-related variation, and college students exhibited a higher level than high school students across LIPSAL. Thirdly, through interview, the students demonstrated a comprehensive understanding of the application of GAI. We found that students have a positive attitude towards GAI and are very willing to use it, which is why GAI has grown so rapidly in popularity. They also told us prospects and challenges in using GAI. In the future, as GAI matures technologically, it will have an greater impact on students. These findings may help better understand usage by different students and inform future research in digital education.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports results from a hybrid survey (questionnaire plus interviews) examining the impact of generative AI (GAI) on students across four grade levels in six LIPSAL areas (learning interest, independent learning, problem solving, self-confidence, appropriate use, learning enjoyment). It claims that GAI has the greatest impact on appropriate use and the lowest on learning interest and self-confidence; that college students score higher than high school students across LIPSAL; and that interviews reveal positive student attitudes toward GAI along with prospects and challenges.
Significance. If substantiated with adequate methodological detail and controls, the multi-grade, multi-area survey could provide useful descriptive data on student perceptions of GAI in education and help guide digital-education policy. The hybrid method and LIPSAL framework are reasonable starting points for exploratory work, but the current absence of basic reporting elements prevents any assessment of whether the stated grade or area differences are reliable.
major comments (3)
- [Abstract / Methods] Abstract and (presumed) Methods section: No sample size, response rate, demographic breakdown, exclusion criteria, or recruitment details are supplied for the questionnaire, so the claims of differential LIPSAL impacts and grade-level differences cannot be evaluated for representativeness or statistical power.
- [Results] Results (questionnaire findings): The assertions that GAI has 'greatest impact on appropriate use' and 'lowest level of learning interest and self-confidence,' and that 'college students exhibited a higher level than high school students across LIPSAL,' are presented without any statistical tests, p-values, effect sizes, or even raw means, rendering the comparative claims unsupported.
- [Methods / Discussion] Methods and Discussion: The hybrid-survey approach is treated as sufficient to attribute LIPSAL changes to GAI, yet the manuscript provides no description of controls for self-report bias, prior GAI exposure, non-GAI comparison groups, or external validation against performance metrics; this directly undermines the central attribution claims.
minor comments (2)
- [Abstract] Abstract: 'researches about student's utilisation' should read 'research on students' utilisation'; 'an greater impact' should read 'a greater impact'.
- [Introduction] The LIPSAL acronym is introduced without an explicit expansion on first use in the main text.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback, which identifies key gaps in reporting and interpretation. We respond to each major comment below and will revise the manuscript to improve clarity and transparency where the study design permits.
read point-by-point responses
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Referee: [Abstract / Methods] Abstract and (presumed) Methods section: No sample size, response rate, demographic breakdown, exclusion criteria, or recruitment details are supplied for the questionnaire, so the claims of differential LIPSAL impacts and grade-level differences cannot be evaluated for representativeness or statistical power.
Authors: We agree these reporting elements were omitted. The revised manuscript will add a dedicated Methods subsection detailing the questionnaire sample size, response rate, demographic breakdown by grade and other variables, exclusion criteria (e.g., incomplete responses), and recruitment procedures through school and university channels. revision: yes
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Referee: [Results] Results (questionnaire findings): The assertions that GAI has 'greatest impact on appropriate use' and 'lowest level of learning interest and self-confidence,' and that 'college students exhibited a higher level than high school students across LIPSAL,' are presented without any statistical tests, p-values, effect sizes, or even raw means, rendering the comparative claims unsupported.
Authors: The claims reflect observed descriptive patterns in the questionnaire responses. In revision we will report the raw means (and standard deviations where available) for each LIPSAL dimension by grade level and explicitly note that the study is exploratory and does not include inferential statistical tests. revision: yes
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Referee: [Methods / Discussion] Methods and Discussion: The hybrid-survey approach is treated as sufficient to attribute LIPSAL changes to GAI, yet the manuscript provides no description of controls for self-report bias, prior GAI exposure, non-GAI comparison groups, or external validation against performance metrics; this directly undermines the central attribution claims.
Authors: The study is a perception survey and does not contain control groups, performance metrics, or explicit bias controls. We will revise the Discussion to state these limitations clearly and reframe results as student-reported perceptions rather than causal attributions to GAI. revision: partial
- Conducting post-hoc statistical tests or adding control groups/performance metrics, as these were not part of the original study design.
Circularity Check
No circularity: empirical survey report with no derivations or self-referential fits
full rationale
The paper reports findings from a hybrid questionnaire-plus-interview survey on student perceptions of GAI across LIPSAL areas and grade levels. No equations, fitted parameters, predictions derived from models, or mathematical derivations appear in the abstract or described method. Claims rest directly on collected self-report data without any reduction to prior self-citations, ansatzes, or input-output equivalences. This is a standard empirical presentation with no load-bearing steps that could be circular by the enumerated patterns.
discussion (0)
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