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RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis
Open accessAbstract
As a new type of artificial intelligence, ChatGPT is becoming widely used in learning. However, academic consensus regarding its efficacy remains elusive. This study aimed to assess the effectiveness of ChatGPT in improving students’ learning performance, learning perception, and higher-order thinking through a meta-analysis of 51 research studies published between November 2022 and February 2025. The results indicate that ChatGPT has a large positive impact on improving learning performance ( g = 0.867) and a moderately positive impact on enhancing learning perception ( g = 0.456) and fostering higher-order thinking ( g = 0.457). The impact of ChatGPT on learning performance was moderated by type of course ( Q B = 64.249, P < 0.001), learning model ( Q B = 76.220, P < 0.001), and duration ( Q B = 55.998, P < 0.001); its effect on learning perception was moderated by duration ( Q B = 19.839, P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course ( Q B = 7.811, P < 0.05) and the role played by ChatGPT ( Q B = 4.872, P < 0.05). This study suggests that: (1) appropriate learning scaffolds or educational frameworks (e.g., Bloom’s taxonomy) should be provided when using ChatGPT to develop students’ higher-order thinking; (2) the broad use of ChatGPT at various grade levels and in different types of courses should be encouraged to support diverse learning needs; (3) ChatGPT should be actively integrated into different learning modes to enhance student learning, especially in problem-based learning; (4) continuous use of ChatGPT should be ensured to support student learning, with a recommended duration of 4–8 weeks for more stable effects; (5) ChatGPT should be flexibly integrated into teaching as an intelligent tutor, learning partner, and educational tool. Finally, due to the limited sample size for learning perception and higher-order thinking, and the moderately positive effect, future studies with expanded scope should further explore how to use ChatGPT more effectively to cultivate students’ learning perception and higher-order thinking.
Overview
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TL;DR
This PRISMA-guided meta-analysis of 51 experimental and quasi-experimental studies (November 2022–February 2025) finds that ChatGPT has a large positive effect on students' learning performance (g = 0.867) and moderate positive effects on learning perception (g = 0.456) and higher-order thinking (g = 0.457). Effects are moderated by course type, learning model, duration, and the role ChatGPT plays, leading the authors to recommend scaffolded, sustained (4–8 week) integration of ChatGPT across grade levels and course types.
Key findings
- Overall effects across 72 independent effect sizes: learning performance g = 0.867 (k = 44, 95% CI [0.683, 1.052], p < 0.001); learning perception g = 0.456 (k = 19, 95% CI [0.287, 0.625], p < 0.001); higher-order thinking g = 0.457 (k = 9, 95% CI [0.255, 0.650], p < 0.001).
- Learning performance was moderated by type of course (Q_B = 64.249, p < 0.001; strongest for skills and competencies development, g = 0.874), learning model (Q_B = 76.220, p < 0.001; strongest for problem-based learning, g = 1.113, weakest for project-based learning, g = 0.239), and duration (Q_B = 55.998, p < 0.001; strongest at 4–8 weeks, g = 0.999). Grade level, role of ChatGPT, and application area were not significant.
- Learning perception was moderated only by duration (Q_B = 19.839, p < 0.001), with >8 weeks showing the largest effect (g = 1.054) versus ≤1 week (g = 0.203).
- Higher-order thinking was moderated by type of course (Q_B = 7.811, p < 0.05; STEM g = 0.737, language/writing g = 0.334, skills development g = 0.296) and role of ChatGPT (Q_B = 4.872, p < 0.05; intelligent tutor g = 0.945).
Methods
- Design: meta-analysis following PRISMA guidelines, with literature search, inclusion/exclusion screening, coding of study characteristics, and data analysis; search covered Web of Science Core Collection, ScienceDirect, Taylor & Francis, Wiley Online Library, and Scopus, restricted to November 2022–February 2025, starting from 6,621 records and yielding 51 included articles.
- Sample: 51 (quasi-)experimental studies with an experimental group using ChatGPT and a control group without it, both using the same learning method; 72 independent effect sizes were extracted. Interrater reliability for study selection was Cronbach's α = 0.96.
- Analysis: Hedges's g computed in Comprehensive Meta-Analysis (3.0/3.3.0), interpreted via Sawilowsky's (2009) thresholds; random-effects models were used given heterogeneity (I² = 89.243% for learning performance, 72.580% for learning perception, 55.813% for higher-order thinking); publication bias assessed via funnel plots and fail-safe N (5490, 399, and 101 respectively), with one-study-removal sensitivity analysis.
Limitations
- Stated by the authors: the sample of 51 documents included only one primary-school study and none at kindergarten level; only 19 and 9 studies examined learning perception and higher-order thinking respectively, versus a much larger sample for learning performance; only seven moderating variables were explored, omitting factors such as cultural background, parents' occupations, and national policies; and only experimental/quasi-experimental studies were included, excluding qualitative and mixed-methods research.
- Evident from the text: heterogeneity was high for learning performance (I² = 89.243%), and the authors note some funnel-plot points fell outside the diagonal lines, indicating potential bias or heterogeneity; several moderator categories (e.g., mixed roles, intelligent partners for higher-order thinking, primary school) were excluded from analyses due to insufficient data.
- Evident from the bibliographic record: the article is labeled "RETRACTED ARTICLE" in the title, though the retraction is not discussed in the full text provided.
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68 Own result · 19 Own method · 17 Not checked yet · 6 No basis given · 5 Own interpretation
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results indicate that ChatGPT has a large positive impact on improving learning performance (g = 0.867) and a moderately positive impact on enhancing learning perception (g = 0.456) and fostering higher-order thinking (g = 0.457).”
- Also stated in: § Results · hedged
“The overall effects of ChatGPT on enhancing learning performance, improving learning perception, and promoting higher-order thinking were calculated as g = 0.867, g = 0.456, and g = 0.457, respectively, indicating that ChatGPT has a large positive effect on learning performance and a medium positive effect on learning perception and higher-order thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Results · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The Q-test for higher-order thinking is 18.105, and the p-value of the Q-test is less than 0.1.The I² value is 55.813%, indicating moderate heterogeneity in the included studies regarding the effect of ChatGPT on higher-order thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“According to Table 4, the Qvalue for learning performance is 339.725, and the p-value of the Q-test is <0.1.The I² value is 89.243%, indicating a high degree of heterogeneity in the included studies regarding the impact of ChatGPT on learning performance.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results indicate that ChatGPT has a large positive impact on improving learning performance (g = 0.867) and a moderately positive impact on enhancing learning perception (g = 0.456) and fostering higher-order thinking (g = 0.457).”
- Also stated in: § Results · hedged
“The overall effects of ChatGPT on enhancing learning performance, improving learning perception, and promoting higher-order thinking were calculated as g = 0.867, g = 0.456, and g = 0.457, respectively, indicating that ChatGPT has a large positive effect on learning performance and a medium positive effect on learning perception and higher-order thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Results · hedged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The impact of ChatGPT on learning performance was moderated by type of course (Q B = 64.249,P < 0.001), learning model (Q B = 76.220,P < 0.001), and duration (Q B = 55.998,P < 0.001); its effect on learning perception was moderated by duration (Q B = 19.839,P < 0.001); and its influence on the development of higher-order thinking was moderated by type of course (Q B = 7.811, P < 0.05) and the role”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 30
- The quote only partly states this claim. 1
- Probably not a substantial claim. 14
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT was found to be beneficial across all time periods, with interventions lasting more than 8 weeks (g = 1.054) showing a substantially larger effect size than those lasting less than 1 week (g = 0.203), 1-4 weeks (g = 0.404), and 4-8 weeks (g = 0.493).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results indicate that ChatGPT has a substantial positive effect on students' learning performance, with an overall effect size of 0.867.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably, compared to its large positive impact on improving learning performance (g = 0.867), ChatGPT has only a moderately positive impact on learning perception.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the improvements in learning performance attributed to ChatGPT are quite strong (g = 0.867), while the improvements in higher-order thinking are only moderate.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The final sample consisted of 51 articles, published between November 2022 and February 2025.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Inclusion criteria
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“this study yielded a unique conclusion: in project-based learning, the effect of ChatGPT on students' learning performance is the weakest, showing only a small positive impact.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“| Mixed | 2 | 0.313[0.024,0.602] | 2.122*** | 1 | | |”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
plain- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The results show that, for learning performance, after the removal of any single study, the range of the 95% confidence interval (CI) of the effect size still remained between 0.462 and 0.574 for the fixedeffects model and between 0.683 and 1.052 for the random-effects model.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In this study, the fail-safe N for learning performance was calculated to be 5490 (5k + 10 = 230, k = 44), for learning perception was calculated to be 399 (5k + 10 = 105, k = 19), and for higher-order thinking was calculated to be 101 (5k + 10 = 55, k = 9), indicating that the possibility of publication bias is relatively small for this study.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In this study, the fail-safe N for learning performance was calculated to be 5490 (5k + 10 = 230, k = 44), for learning perception was calculated to be 399 (5k + 10 = 105, k = 19), and for higher-order thinking was calculated to be 101 (5k + 10 = 55, k = 9), indicating that the possibility of publication bias is relatively small for this study.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In this study, the fail-safe N for learning performance was calculated to be 5490 (5k + 10 = 230, k = 44), for learning perception was calculated to be 399 (5k + 10 = 105, k = 19), and for higher-order thinking was calculated to be 101 (5k + 10 = 55, k = 9), indicating that the possibility of publication bias is relatively small for this study.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The effect values for the impact of ChatGPT on learning performance, learning perception, and higher-order thinking are relatively evenly distributed on both sides of the funnel plot, indicating that the data have sufficiently high reliability to be used for meta-analysis.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All relevant studies were conducted at the college level, and thus no analysis was performed for grade level.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, the experimental and control groups had to adopt the same learning method.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Inclusion criteria
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT can make a course more interesting and encourage students to actively search for information, ultimately contributing to an increase in learning perception.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The literature inclusion and exclusion criteria were developed by two researchers specializing in educational technology, and are presented in Table 1.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Method
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This finding agrees with the results of Guan et al. (2024) and may be attributed to the consistent positive feedback students receive from ChatGPT during extended usage.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Guan et al. 2024 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The various roles it can play and the areas in which it can be applied are essentially manifestations of its core function as a learning support tool.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“This may be because such courses typically involve well-defined task objectives and procedural steps; ChatGPT provides immediate feedback, targeted guidance, and problemsolving support (Rakap, 2023;Urban et al. 2024), and thus can enable students to complete such tasks more efficiently.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Rakap, 2023
- Not checked yetcites Urban et al. 2024
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be noted that the primary school sample was excluded from the analysis because it consisted of only one study, which did not find a statistically significant effect.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Moreover, ChatGPT-generated content is not always completely accurate, and this bias can hinder the development of students' higherorder thinking (Niloy et al. 2023), thus limiting the overall degree to which ChatGPT can promote students' higher-order thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Niloy et al. 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It thus may lack critical analysis abilities and the ability to provide creative problem-solving solutions (Lu et al. 2024).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Lu et al. 2024
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: ChatGPT is based on pretrained models that rely on existing data and patterns.
In the paper
“This may be because the prolonged use of ChatGPT can lead students to become overly reliant on the AI tool, and thus they may neglect to reinforce the knowledge they have learned (Agarwal, 2023), which, in turn, would result in a decline in learning performance.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Agarwal, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These features enable ChatGPT to support more diverse learning scenarios, which may, in turn, contribute to its larger effect size.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“This may be because project-based learning emphasizes the completion of a comprehensive project in a realworld context (Botha, 2010).In such a scenario, ChatGPT's support for addressing complex problem-solving tasks may be limited, resulting in only a minor effect.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Botha, 2010
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Since differences in sample sizes between studies might introduce bias into the calculated effect sizes, Hedges's g was used to adjust the effect sizes to correct for small sample size biases (Hedges, 1981).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Data analysis.
What the sources say
- Not checked yetcites Hedges, 1981 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“This may be attributed to its inherent strengths, in that ChatGPT excels at providing clear explanations for a wide range of problems, whether establishing simple correspondences between geometric structures and algebraic equations (Poola and Božić, 2023) or explaining complex scientific concepts (Ali et al. 2024).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Poola and Božić, 2023
- Not checked yetcites Ali et al. 2024 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Meta-analysis can be used to comprehensively analyze the results of multiple independent research studies that are focused on the same research purpose, reveal the differences and commonalities among them, and thereby draw universal and regular conclusions (Lipsey and Wilson, 2001).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Introduction C
What the sources say
- Not checked yetcites Lipsey and Wilson, 2001
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In STEM and related courses, students are often required to collaborate with ChatGPT to complete complex projects (Li, 2023;Li et al. 2024), and the process of completing such projects inherently promotes the development of higher-order thinking skills such as creative thinking, problem-solving, and critical thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Li, 2023
- Not checked yetcites Li et al. 2024
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may be due to the fact that when students first started using ChatGPT, they lacked the questioning skills needed to use it effectively.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Articles that met the coding criteria were independently selected by two researchers, and acceptable interrater reliability was achieved (Cronbach's α = 0.96).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Inclusion criteria
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“When using ChatGPT, students continuously reflect on their learning processes and adjust their learning strategies in real-time.This process effectively fosters the development of students' higherorder thinking skills (Lu et al. 2024).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > How effective is ChatGPT in promoting learning performance?
What the sources say
- Not checked yetcites Lu et al. 2024
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, this study only selected experimental and quasi-experimental studies and did not include relevant qualitative or mixed-methods research.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, this study sample size was limited, comprising only 51 documents, of which only one addressed primary school and none addressed kindergarten.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, this study only explored seven moderating variables, but there are many more variables that could potentially affect the impact of ChatGPT on student learning, such as students' cultural backgrounds, parents' occupations, and relevant national policies.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, the number of included samples that examined learning perception and higher-order thinking was relatively small, with only 19 and 9 studies, respectively, compared to a significantly larger sample for learning performance.This imbalance may limit our ability to comprehensively understand ChatGPT's impact on learning perception and higher-order thinking.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
ChatGPT is based on pretrained models that rely on existing data and patterns.
“ChatGPT is based on pretrained models that rely on existing data and patterns.”
The paper relies on this without support.
The meta-analysis by Chen et al. (2025) on other AI assessment tools is comparable to this meta-analysis on ChatGPT.
“The results indicate that the effect size for the impact of ChatGPT on students' learning performance is significantly larger than that of other AI assessment tools (g = 0.390).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Their study also found a large effect on learning performance, particularly when structured frameworks like Bloom's Taxonomy are used to guide AI-supported tasks.”
From Enhancing service-learning through Generative AI: a mixed-methods study on educational Game design in a finance course (2025) · cites this paper as Wang and Fan (2025)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Wang and Fan (2025) found that ChatGPT has a moderate effect on higher-order thinking, but only when appropriate scaffolding is present, such as when instructors position ChatGPT as a learning partner.”
From Enhancing service-learning through Generative AI: a mixed-methods study on educational Game design in a finance course (2025) · cites this paper as Wang and Fan (2025)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“much of the literature on AI-supported feedback has focused on general writing tasks or broad comparisons among feedback sources, whereas disciplinary reasoning in university science courses has received less sustained attention”
From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Wang & Fan, 2025
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Wang and Fan (2025) analysed studies from a similar period and reported comparable findings to those of Deng and colleagues: a substantial positive effect of ChatGPT on learning.”
From ChatGPT in Education: An Effect in Search of a Cause (2025) · cites this paper as Wang and Fan (2025)
The cited source says what the claim attributes to it.
In the citing paper
“which broadens forms of expression, strengthens creativity [4,5], and supports higher-order and critical thinking [6,7].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“Existing evidence suggests that GenAI can support learning performance, learning perceptions, and higher-order thinking, but these benefits vary substantially depending on the role assigned to the tool, the instructional design, and the characteristics of the task”
From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Wang & Fan, 2025
The cited source says what the claim attributes to it.
In the citing paper
“More broadly, the study adds nuance to recent syntheses of GenAI in higher education by suggesting that apparent benefits are highly design-sensitive and that concerns about authorship, evidence of learning, and learner responsibility are not only matters of policy or integrity, but also matters of feedback design(Wang & Fan, 2025;Xia et al., 2024).”
From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Wang & Fan, 2025
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“immediate product improvement is often examined without simultaneously investigating feedback uptake, self-regulated learning, and later performance without AI support, making it difficult to determine whether observed gains reflect durable learning, stronger revision processes, or temporary performance support”
From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Wang & Fan, 2025
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Coding of study characteristics.
“A total of six moderating variables were selected (see Table 2).”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
“Second, this study only explored seven moderating variables, but there are many more variables that could potentially affect the impact of ChatGPT on student learning”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| >8 week | 13 | 0.531[0.490,0.684] | 11.912*** | 12 | | |”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
Reported 0.531 · recomputed 0.587
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“| Language learning and academic writing | 12 | 0.531[0.310,0.531] | 7.454*** | 11 | | |”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
Reported 0.531 · recomputed 0.4205
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Data availability
“No datasets were generated or analysed during the current study.”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Coding of study characteristics.
“Ultimately, the coding process resulted in the extraction of 72 independent effect sizes from the selected 51 articles.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Results
“According to Table 4, the Qvalue for learning performance is 339.725”
§ The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis > Limitations and future research
“| Learning performance | 44 | 0.867 | 0.683 | 1.052 | 8.458 | <0.001 | 399.725 | 43 | 89.243 | <0.001 |”
Citations
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283
Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1×
“The technology acceptance model posits that perceived usefulness and perceived ease of use in fl uence an individual ’ s intention or attitude toward using new technology (Venkatesh and Bala, 2008).”
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 3×
“…studies have claimed that ChatGPT can improve students ’ learning performance, learning perception, and higher-order thinking (Jalil et al. 2023; Kasneci et al. 2023; Lu et al. 2024), but other studies have found that it does not signi fi cantly improve students ’ learning performance or…”
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Hedges, L. V. (1981) · Journal of Educational Statistics · doi:10.2307/1164588 · Cited by 5,695
Schulz (1995) · doi:10.1001/jama.1995.03520290060030 · Cited by 5,634
New Effect Size Rules of Thumb
Sawilowsky, S. S. (2009) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1257035100 · Cited by 3,254
Research works printed but not linked: 5
- Alsharif MHK, Elamin AY, Almasaad JM, Bakhit NM, Alarifi A, Taha KM, Hassan WA, Zumrawi E (2024) Using ChatGPT to create engaging problem-based learning scenarios in anatomy: a step-by-step guide. New Armen Med J 18(4). https://doi.org/10.56936/18290825-4.v18.2024-98 · DOI
- Lipsey MW, Wilson DB (2001) Practical meta-analysis. Sage Publications, Inc
- Luo H, Liao X, Ru Q, Wang Z (2024) Generative AI-supported teacher comments: an empirical study based on junior high school mathematics classrooms. e-Educ Res 45(5):58–66. https://doi.org/10.13811/j.cnki.eer.2024.05.008 · DOI
- Poola I, Božic V (2023) Guiding AI with human intuition for solving mathematical problems in ChatGPT. Int J Eng Sci Res 11(07):31–40. https://www.researchgate.net/publication/373447147
- Vygotskij LS, Hanfmann E (1966) Thought and language. MIT Press
Cited by
Sorted by citations
Items 401–463 of 463
Integrating ChatGPT into Student Research Practices
Гулай, О. І., Shemet, V. Y., & Фурс, Т. В. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-31853-4_16
Sembayev, T., Tsekoyev, P., Тайболатов, Қ. М., & Nauryzbayev, B. A. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649568
Mansour, O. E., Fenno, C., & Jerdon, G. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--59836
AI-Augmented Creativity in Personalized Learning Pathways
Eziokwu, U. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch007
Pair Programming with AI: A Systematic Literature Review
Smith, J. M. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--60384
The construction of the Chinese AI version of Digital Bloom (CAID Bloom)
ZHOU, D., & Liu, X. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--60373
Zhuang, X., Liu, B., Yiting, S., Li, S., & Liu, B. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649684
Khoury, C. E., Sabbouh, T. M., Palazzolo, C., Bahous, S. A., & HARMANANI, H. M. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--59516
Long-term impact of ChatGPT-4 on music students' creative and analytical skills: A follow-up study
Zhou, W., & Kim, Y. (2026) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2026.102344
Mahlaba, S. C. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1783496 · Cited by 1
Tsiligkiris, V. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00617-6
Educating Future Engineers to be Responsible Citizens
Derda, M., & Wedel, M. (2026) · Athens Journal of Τechnology & Engineering · doi:10.30958/ajte.13-3-1
Jiang, M., Yang, D., So, C. C., Loh, A. W.-K., Chau, C. P., & Yung, S. P. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663629
Sioulas, P.-V., & Drakopoulos, V. (2026) · The Social Studies · doi:10.1080/00377996.2026.2704057
IMPLEMENTASI CHATBOT BERBASIS ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN MAHARAH KITABAH MAHASISWA
Jd., A. S. C. S., Supriyadi, M., & Fatmawati, F. (2026) · Al-Fashahah Journal of Arabic Education Linguistics and Literature · doi:10.59562/al-fashahah.v6i1.84316
Çiğdem, H. (2026) · Journal Human Research in Rehabilitation · doi:10.21554/hrr.092623
Reconceptualizing postsecondary adaption to GenAI within the turn toward digital life
Pedersen, I. (2026) · Didaktik Deutsch · doi:10.21248/dideu.957
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Friederichs, H. (2026) · doi:10.59350/p0n7d-1qe84
Ardi, H., Putra, R. D., Syafei, A. F. R., Safitri, D., & Nurjanah, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10387304/v1
Lee, E. (2026) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2026.29.8.001
Hsieh, H.-M., Maritz, A., & Shieh, C.-J. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/19238
Torres, T. H. (2026) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v10i4.25571
HUBUNGAN PERSEPSI PEMANFAATAN CHATGPT DENGAN PARTISIPASI BELAJAR DALAM PEMBELAJARAN SEJARAH
Mahmudin, M. A., & Purwaningsih, S. M. (2026) · LEARNING Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran · doi:10.51878/learning.v6i4.14694
Edo, G. I., Omoriwhovo Enoch., O., Akpo, M. O., Emumejaye, K., Iwanegbe, I., Oghroro, E. E. A., Owheruo, J. O., Akpoghelie, P. O., Yousif, E., Jikah, A. N., et al. (2026) · Food Research International · doi:10.1016/j.foodres.2026.120811
Abhishek, N. A., Nayak, K. M., Divyashree, M. S., & Jain, U. (2026) · Frontiers of digital education. · doi:10.1007/s44366-026-0102-2
Soni, D., Sankathala, S., & Das, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0383-1.ch002
Cárdenas, R. A. P., Alvarado, C. I. L., Mendía, K. A. Z., & Pérez, R. J. P. (2026) · Research Club Scientia · doi:10.65415/zzxsgn08
Viet, H. H., & Bui, H. M. (2026) · Technology in Language Teaching & Learning · doi:10.29140/tltl.2026.104438
Zhang, Y., & Fu, P. (2026) · International Conference on Computational Intelligence and Machine Learning (CIML) · doi:10.1109/ciml69155.2026.11692804
Fang, L., Li, Z., Xiong, B., & Lu, Z. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1832451
Karampelas, K. (2026) · Journal of ExoTechnology and Education · doi:10.36615/q7ez7g71
Albaqami, H. M. R. (2026) · University of Tabuk Journal for Humanities and Social Sciences · doi:10.68114/1658-8797.1019
Critical Consciousness Development & Preservation in Black Youth in the Era of Generative AI
Zukeri, K., & Brookins, B. (2026) · Freedom A Journal of Research in Africana Studies · doi:10.65373/xyku2174
Глухов, А. П., & Станиславовна, С. Е. (2026) · Вестник Томского государственного педагогического университета · doi:10.23951/1609-624x-2026-5-46-55
Su, K.-D. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2735911
Islam, A. (2026) · Revista Romaneasca pentru Educatie Multidimensionala · doi:10.66388/rrem/18.3/57
Herrero, A. C., & Murillo, F. J. (2026) · Edutec Revista Electrónica de Tecnología Educativa · doi:10.21556/edutec.2026.97.4527
Pavlović, Z. G., Savić, A., Radičević, V., & Vasiljević, R. (2026) · IEEE Lasers and Electro-Optics Society Annual Meeting (LEOS) · doi:10.46793/et26.d09p
Integrating ChatGPT into 5E inquiry-based learning to scaffold EFL academic research writing
Cheng, Y.-H. (2026) · Asian-Pacific Journal of Second and Foreign Language Education · doi:10.1186/s40862-026-00455-z · Cited by 1
Language Learners Select Dictionaries or AI Tools Depending on the Task
Ptasznik, B., & Lew, R. (2026) · Teaching English With Technology · doi:10.56297/vaca6841/rumj3456/mzlg7942
AI in Upper Secondary Classrooms: Innovation, Gatekeeping, and Student Use
Bergdahl, N. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-39734-8_12
GENERATIVE AI AVAILABILITY AND METACOGNITIVE CALIBRATION: EVIDENCE FROM HIGHER EDUCATION
(2026) · Issues in Information Systems · doi:10.48009/1_iis_2026_109
Myers, R. W., Ramachandran, K., & Varadarajan, B. (2026) · INFORMS Transactions on Education · doi:10.1287/ited.2025.0177
Yang, H., Xu, N., Shao, Y., 杨海明, & Zhang, W. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2739744
Value-Added Assessment for AI-Assisted Assignments
Braude, E. J., & Mansur, W. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33123-6_5
Pasividad, mentalidad técnica y formación
Congote, L. M. G., & Guillén, G. V. (2026) · Conjectura filosofia e educação · doi:10.18226/21784612.v31.e025023
Determinants and Impact of AI-Based Chatbot Adoption in Higher Education: A TAM–ISSM Perspective
Gelibolu, M. F. (2026) · SAGE Open · doi:10.1177/21582440261495170
A Behavioural Assessment of Factors Shaping AI Prompting Adoption in Academic Settings
Gopalakrishna, V., Kappagantula, M., Thyadi, N. P., Venugopal, K., Terlapu, P. V., & Santhikumar, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-4664-7.ch002
Al‐Barakat, A. A., AlAli, R. M., Aboras, A., Alribdi, N. I., Abdullatif, A. K. A., & Zaher, A. M. N. (2026) · Scientific Reports · doi:10.1038/s41598-026-73410-0
Mujahidin, A., & Hakim, A. (2026) · INNOVASI JURNAL INOVASI PENDIDIKAN · doi:10.64540/k969r880
Kurose, D., Tsutsumi, K., Takaoka, R., Nakata, M., Ahama, S., & Mukaida, N. (2026) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt68022.2026.00020
Dilinika, J. M. S. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2747110
Barakabitze, A. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7643464
Who benefits from LLMs? Prompt engineering competence and equity in higher education
Li, Z., Liu, Y., Jiang, J., Zhang, Z., Li, Z., & Chen, W. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00633-6
الزهراني, ن. ب. أ. ب. ع. (2026) · Journal of Arts, Literature, Humanities and Social Sciences · doi:10.33193/jalhss.133.2026.1739
The Effects of Generative AI on Critical Thinking in Educational Contexts: A Systematic Review
Khales, S. (2026) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2026.101000091
Dušek, M., Bartovčak, G. K., & Frank, D. (2026) · IEEE International Symposium on Intelligent Systems and Informatics (SISY) · doi:10.1109/sisy71351.2026.11738564
Social and Pedagogical Drivers of ChatGPT Acceptance: An Extended TAM Model in Teacher Education
Karalar, H., Altıntaş, S., & Altan, B. A. (2026) · Participatory Educational Research · doi:10.17275/per.26.82.13.6
Stability and boundary conditions of extended TAM mechanisms in DeepSeek adoption
Wang, N., Jiang, B., Wang, J., Chang, C., & Zhu, S. (2026) · Telematics and Informatics · doi:10.1016/j.tele.2026.102482
Self-citationExploring AI as a reading companion: L2 learners' approaches to literary comprehension
Strietholt, S., Kaltenbach, L., Huang, B. H., & Powell, N. R. (2026) · Foreign Language Annals · doi:10.1111/flan.70084
The impact of peer tutoring on students’ higher-order thinking: A meta-analysis
Zhang, C., Zhou, Z., Ma, L., Liu, H., & Huang, Q. (2026) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2026.102416
Impact of widespread LLM (AI) availability on student exam performance
Kim, M. H. (2026) · Journal of Education for Business · doi:10.1080/08832323.2026.2752354
Metadata
- Authors
- Wang, J., & Fan, W.
- Affiliations
- Hangzhou Normal University (CN)
- Corresponding author
- Fan, Wenxiang
- Venue
- Humanities and Social Sciences Communications
- Published
- 2025-05-06
- Volume
- 12
- Issue
- 1
- ISSN
- 2055-1045 (ISSN-L)
- 2662-9992
- OpenAlex
- W4410129808
- Semantic Scholar
- 45606f671465c499d9299234e8c5a3f444556f1b
- Cited by
- 322
- References
- 134
- Citations by year
- 2026: 217 · 2025: 105
- FWCI
- 48.98
- Type
- journal article
- Language
- en
- Status
- Retracted
- Update notices
- retraction (2026-04-22)
- Open access
- gold
- doi.org
- Licence
- Locations
- Humanities and Social Sciences Communications (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Major Project of Humanities and Social Sciences Key Research Base of the Ministry of Educatio (22JJD880023)
- University-Industry Collaborative Education Program (240904171111322)
- Hangzhou Social Science Routine Planning Project (Z24JC014)
- Hangzhou Normal University’s 2023 Teaching Construction and Reform Project “Exploration and Practice of Generative Artificial Intelligence to Promote University Teaching Reform”
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, generative AI in education, learning performance, perceptions of learning, higher-order thinking skills, AI-assisted learning, problem-based learning, Bloom's taxonomy, instructional scaffolding, intelligent tutoring systems, moderating effect
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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