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RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis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
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 1,435 · cited 1×
“This may be because ChatGPT provides such broad learning support, including extensive knowledge access, real-time interaction, personalized assistance, diverse resources, and data analysis services (Ali et al. 2024; Luo et al. 2024; Rawas, 2024; Yan et al. 2024).”
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 1×
“Conversely, other scholars have argued that ChatGPT hinders students ’ learning performance, learning perception, and higher-order thinking (Hays et al. 2024; Rahman and Watanobe, 2023).”
Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048 · cited 1×
“Most review studies have examined the effectiveness of ChatGPT in education (Ansari et al. 2024; Baig and Yadegaridehkordi, 2024; Chen et al. 2024; Grassini, 2023).”
Page, M. J., & Moher, D. (2017) · Systematic Reviews · doi:10.1186/s13643-017-0663-8 · Cited by 769
Collaborating with ChatGPT in argumentative writing classrooms
Su, Y., Lin, Y., & Lai, C. (2023) · Assessing Writing · doi:10.1016/j.asw.2023.100752 · Cited by 760
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745
Song, C., & Song, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1260843 · Cited by 717
Zhang, X., Chen, Y., Hu, L., & Wang, Y. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1016300 · Cited by 637
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13454 · Cited by 591
AI-generated feedback on writing: insights into efficacy and ENL student preference
Escalante, J., Pack, A., & Barrett, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00425-2 · Cited by 589
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531
The impact of ChatGPT on higher education
Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1206936 · Cited by 519 · cited 1×
“Dempere et al. (2023), through a systematic literature review, found that ChatGPT performs poorly in solving complex reasoning problems, fails to provide emotional support, and may negatively affect learning by reducing peer interaction.”
Imran, M., & Almusharraf, N. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13605 · Cited by 421
Impact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study
Mahapatra, S. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00295-9 · Cited by 398
ChatGPT improves creative problem-solving performance in university students: An experimental study
Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105031 · Cited by 396
The Influence of ChatGPT on Student Engagement: A Systematic Review and Future Research Agenda
Lo, C. K., Hew, K. F., & Jong, M. S.-Y. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105100 · Cited by 392
The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach
Mai, D. T. T., da, C. A. N., & Hanh, N. V. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1328769 · Cited by 363
ChatGPT: Empowering lifelong learning in the digital age of higher education
Rawas, S. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12114-8 · Cited by 310
Wu, T., Lee, H., Li, P., Huang, C.-N., & Huang, Y. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231191125 · Cited by 302
Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100198 · Cited by 296
Xiao, Y., & Zhi, Y. (2023) · Languages · doi:10.3390/languages8030212 · Cited by 289
A systematic review of ChatGPT use in K‐12 education
Zhang, P., & Tur, G. (2024) · European Journal of Education · doi:10.1111/ejed.12599 · Cited by 280
ChatGPT and Software Testing Education: Promises & Perils
Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023) · 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw58534.2023.00078 · Cited by 270 · cited 1×
“Many 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…”
The impact of ChatGPT feedback on the development of EFL students’ writing skills
Poláková, P., & Ivenz, P. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2410101 · Cited by 246
How ChatGPT impacts student engagement from a systematic review and meta-analysis study
Heung, Y. M. E., & Chiu, T. K. F. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100361 · Cited by 229
Lin, X. Y. (2023) · Adult Learning · doi:10.1177/10451595231184928 · Cited by 227
Empowering education development through AIGC: A systematic literature review
Chen, X., Hu, Z., & Wang, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12549-7 · Cited by 225
ChatGPT in the higher education: A systematic literature review and research challenges
Baig, M. I., & Yadegaridehkordi, E. (2024) · International Journal of Educational Research · doi:10.1016/j.ijer.2024.102411 · Cited by 208 · cited 1×
“Most review studies have examined the effectiveness of ChatGPT in education (Ansari et al. 2024; Baig and Yadegaridehkordi, 2024; Chen et al. 2024; Grassini, 2023).”
Students' perceptions of using ChatGPT in a physics class as a virtual tutor
Ding, L., Li, T., Jiang, S., & Gapud, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00434-1 · Cited by 208
Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12223-4 · Cited by 198
Is Chatgpt a menace for creative writing ability? An experiment
Niloy, A. C., Akter, S., Sultana, N., Sultana, J., & Rahman, S. I. U. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12929 · Cited by 198
Sung, Y., Yang, J.-M., & Lee, H.-Y. (2017) · Review of Educational Research · doi:10.3102/0034654317704307 · Cited by 197
Chen, C., & Chang, C.-L. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12553-x · Cited by 196
Sun, D., Boudouaia, A., Zhu, C., & Li, Y. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00446-5 · Cited by 190
Better by You, better than Me? ChatGPT-3 as writing assistance in students’ essays.
Bašić, Ž., Banovac, A., Kružić, I., & Jerković, I. (2023) · doi:10.35542/osf.io/n5m7s · Cited by 189
Boudouaia, A., Mouas, S., & Kouider, B. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241247465 · Cited by 186
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.2015693 · Cited by 183
ChatGPT in physics education: A pilot study on easy-to-implement activities
Bitzenbauer, P. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13176 · Cited by 181
Gallent Torres, C., Zapata González, A., & Ortego Hernando, J. L. (2023) · RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa · doi:10.30827/relieve.v29i2.29134 · Cited by 170
Can ChatGPT effectively complement teacher assessment of undergraduate students’ academic writing?
Lu, Q., Yao, Y., Xiao, L., Yuan, M., Wang, J., & Zhu, X. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2301722 · Cited by 170
Self-citationLi, H. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8923 · Cited by 159
Yu, C., Yan, J., & Cai, N. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1354929 · Cited by 157
Ghafouri, M., Hassaskhah, J., & Zafarghandi, A. M. (2024) · Language Teaching Research · doi:10.1177/13621688241239764 · Cited by 154
Are Lesson Plans Created by ChatGPT More Effective? An Experimental Study
Karaman, M. R., & Göksu, İ. (2024) · International Journal of Technology in Education · doi:10.46328/ijte.607 · Cited by 149
ChatGPT in Teaching and Learning: A Systematic Review
Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024) · Education Sciences · doi:10.3390/educsci14060643 · Cited by 146
Pan, M., Lai, C., & Guo, K. (2024) · The Internet and Higher Education · doi:10.1016/j.iheduc.2024.100991 · Cited by 145
Lu, J., Zheng, R., Gong, Z., & Xu, H. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3369690 · Cited by 144
Young, J. C., & Shishido, M. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140607 · Cited by 144
Scaffolding Computational Thinking With ChatGPT
Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3392896 · Cited by 144
The Impact of Large Language Models on Programming Education and Student Learning Outcomes
Jošt, G., Taneski, V., & Karakatič, S. (2024) · Applied Sciences · doi:10.3390/app14104115 · Cited by 143 · cited 1×
“Therefore, studies with non-experimental or non-quasi-experimental designs were excluded, such as Jo š t et al. (2024).”
Alneyadi, S., & Wardat, Y. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13417 · Cited by 142
Kosar, T., Ostojić, D., Liu, Y. D., & Mernik, M. (2024) · Mathematics · doi:10.3390/math12050629 · Cited by 140
Hartley, K., Hayak, ., & Ko, U. H. (2024) · Education Sciences · doi:10.3390/educsci14020120 · Cited by 136
Li, T., Ji, Y., & Zhan, Z. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305163 · Cited by 133 · cited 2×
“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,…”
Impacts of ChatGPT-assisted writing for EFL English majors: Feasibility and challenges
Tsai, C.-Y., Lin, Y.-T., & Brown, I. K. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12722-y · Cited by 133
Can ChatGPT support prospective teachers in physics task development?
Küchemann, S., Steinert, S., Revenga, N., Schweinberger, M., Dinc, Y., Avila, K. E., & Kuhn, J. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.020128 · Cited by 132
Students’ appraisals post-ChatGPT use: Students’ narrative after using ChatGPT for writing
Nugroho, A., Andriyanti, E., Widodo, P., & Mutiaraningrum, I. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2024.2319184 · Cited by 117
Abuhassna, H., & Alnawajha, S. (2023) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe13040054 · Cited by 116
Huang, K.-L., Liu, Y., & Dong, M.-Q. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101508 · Cited by 112
Students’ Experiences of Using ChatGPT in an Undergraduate Programming Course
Haindl, P., & Weinberger, G. (2024) · IEEE Access · doi:10.1109/access.2024.3380909 · Cited by 108
Abuhassna, H., Yahaya, N., Zakaria, M. A. Z. M., Zaid, N. M., Samah, N. A., Awae, F., Chee, K. N., & Alsharif, A. H. (2023) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2023.13.1.1788 · Cited by 104
Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education
Zeng, H., Zewu, Z., Hu, J., & Cui, Y. (2025) · BMC Medical Education · doi:10.1186/s12909-024-06321-1 · Cited by 102
Understanding students’ behavior in online social networks: a systematic literature review
Masrom, M. B., Busalim, A. H., Abuhassna, H., & Mahmood, N. H. N. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00240-7 · Cited by 101
Darmawansah, D., Rachman, D., Febiyani, F., & Hwang, G. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12986-4 · Cited by 96
Hamid, H., Zulkifli, K., Naimat, F., Che Yaacob, N. L., & Ng, K. W. (2023) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2023.10.001 · Cited by 87
Jauhiainen, J. S., & Garagorry Guerra, A. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1288723 · Cited by 87
Divito, C. B., Katchikian, B. M., Gruenwald, J. E., & Burgoon, J. M. (2023) · Medical Teacher · doi:10.1080/0142159x.2023.2290997 · Cited by 84
Park, H., & Ahn, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642785 · Cited by 84
How do students use ChatGPT as a writing support?
Levine, S., Beck, S. W., Mah, C., Phalen, L., & PIttman, J. (2025) · Journal of Adolescent & Adult Literacy · doi:10.1002/jaal.1373 · Cited by 83 · cited 1×
“At the level of secondary education, scholars have primarily explored the use of ChatGPT to assist students in solving dif fi cult problems (Bitzenbauer, 2023; Levine et al. 2025; Yang et al. 2025) and support knowledge review (Wu et al. 2023).”
Yang, T.-C., Hsu, Y.-C., & Wu, J.-Y. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2450659 · Cited by 80
Effectiveness of Using ChatGPT as a Tool to Strengthen Benefits of the Flipped Learning Strategy
Huesca, G., Martínez-Treviño, Y., Espinosa, J. M. M., Sanromán-Calleros, A. R., Martínez-Román, R., Cendejas-Castro, E. A., & Bustos, R. (2024) · Education Sciences · doi:10.3390/educsci14060660 · Cited by 77
Integrating ChatGPT in Orthopedic Education for Medical Undergraduates: Randomized Controlled Trial
Gan, W., Ouyang, J., Li, H., Xue, Z., Zhang, Y., Dong, Q., Huang, J., Zheng, X., & Zhang, Y. (2024) · Journal of Medical Internet Research · doi:10.2196/57037 · Cited by 73 · cited 1×
“Some scholars argue for its bene fi ts (Boudouaia et al. 2024; Gan et al. 2024; Zhou and Kim, 2024).”
An, S., Zhang, S., Guo, T., Lu, S., Zhang, W., & Cai, Z. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105227 · Cited by 73
Guan, L., Li, S., & Gu, M. M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100323 · Cited by 72
Khurma, O. A., Albahti, F., Ali, N., & Bustanji, A. (2024) · Contemporary Educational Technology · doi:10.30935/cedtech/14334 · Cited by 72
Rakap, S. (2023) · Journal of Special Education Technology · doi:10.1177/01626434231211295 · Cited by 71
Enhancing students’ learning experience in mathematics class through ChatGPT
Gouia‐Zarrad, R., & Gunn, C. L. (2024) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/14614 · Cited by 71
Zhou, W., & Kim, Y. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12705-z · Cited by 68
The Potentials of ChatGPT for Language Learning: Unpacking its Benefits and Limitations
Nugroho, A., Putro, N. H. P. S., & Syamsi, K. (2023) · Register Journal · doi:10.18326/register.v16i2.224-247 · Cited by 68
Abuhassna, H., & Alnawajha, S. (2023) · Education Sciences · doi:10.3390/educsci13020112 · Cited by 67
Chen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13064 · Cited by 67
Rahimi, A. R., Sheyhkholeslami, M., & Pour, A. M. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100539 · Cited by 66
Education big data and learning analytics: a bibliometric analysis
Samsul, S. A., Yahaya, N., & Abuhassna, H. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02176-x · Cited by 65
Albdrani, R. N., & Al-Shargabi, A. A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01411122 · Cited by 62
The Chatbots’ Challenge to Education: Disruption or Destruction?
Birenbaum, M. (2023) · Education Sciences · doi:10.3390/educsci13070711 · Cited by 61
The impact of virtual technology on students’ creativity: A meta-analysis
Wang, Y., Liu, W., Yu, X., Li, B., & Wang, Q. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105044 · Cited by 60
Fokides, E., & Peristeraki, E. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12912-8 · Cited by 59
Ba, H., zhang, L., & Yi, Z. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05565-1 · Cited by 58 · cited 2×
“The meta-analysis results show that ChatGPT effectively enhances student learning performance across different types of courses, as has been shown by prior studies (Ba et al. 2024; Li, 2023; Song and Song, 2023).”
ChatGPT as an innovative heutagogical tool in medical education
Saleem, N., Mufti, T., Sohail, S. S., & Madsen, D. Ø. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2332850 · Cited by 51
Maurya, R. K. (2024) · Counselling and Psychotherapy Research · doi:10.1002/capr.12699 · Cited by 51
Looi, C., & Jia, F. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13369-z · Cited by 50
AI-assisted audio-learning improves academic achievement through motivation and reading engagement
Jafarian, N. R., & Kramer, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100357 · Cited by 49
Mastering medical terminology with ChatGPT and Termbot
Hsu, M.-H. (2024) · Health Education Journal · doi:10.1177/00178969231197371 · Cited by 47
Woo, D. J., Wang, Y., Susanto, H., & Guo, K. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231175999 · Cited by 44
Wang, W.-S., Lin, C.-J., Lee, H.-Y., Huang, Y.-M., & Wu, T.-T. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2024.2375644 · Cited by 43 · cited 2×
“…of modern information technologies, some researchers have attempted to use that technology to create intelligent learning environments that support students ’ construction of knowledge and facilitate the occurrence of effective learning (Rapti et al. 2023; Wang et al. 2024b; Zhang et al. 2022).”
Botha, M. (2010) · Education as Change · doi:10.1080/16823206.2010.522059 · Cited by 41
Familiarity with ChatGPT Features Modifies Expectations and Learning Outcomes of Dental Students
Roganović, J. (2024) · International Dental Journal · doi:10.1016/j.identj.2024.04.012 · Cited by 40
Hays, L., Jurkowski, O., & Sims, S. K. (2024) · TechTrends · doi:10.1007/s11528-023-00924-z · Cited by 40
Ji, Y., Zou, X., Li, T., & Zhan, Z. (2023) · International Conference on Educational Technology Management (ICETM) · doi:10.1145/3637907.3637948 · Cited by 37
Better by you, better than me, chatgpt3 as writing assistance in students essaysPreprint
Bašić, Ž., Banovac, A., Kružić, I., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.04536 · Cited by 35
Shang, S., & Geng, S. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12996 · Cited by 34
Almohesh, A. R. I. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7641 · Cited by 33
Çoban, A., Dzsotjan, D., Küchemann, S., Durst, J., Kühn, J., & Hoyer, C. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00310-z · Cited by 33
Martínez, R. A., Gajderowicz, T. J., & Rodríguez, V. G. G. (2024) · Revista de Educación a Distancia (RED) · doi:10.6018/red.604621 · Cited by 32
Johnson, D. M., Doss, W., & Estepp, C. M. (2024) · Journal of Research in Technical Careers · doi:10.9741/2578-2118.1152 · Cited by 31
Avsheniuk, N., Lutsenko, O., Svyrydiuk, T., & Seminikhyna, N. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.14 · Cited by 31
Leite, B. S. (2023) · Química Nova · doi:10.21577/0100-4042.20230059 · Cited by 30
Chan, S., Lo, N., & Wong, A. (2024) · rEFLections · doi:10.61508/refl.v31i3.277514 · Cited by 29 · cited 1×
“In terms of assessment, studies have examined the use of ChatGPT in grading student work (Chan et al. 2024; Su et al. 2023) and providing targeted, personalized feedback (Escalante et al. 2023; Qi et al. 2024 (Albdrani and Al-Shargabi, 2023; Looi and Jia, 2025) and recommending diverse learning…”
The Impact of ChatGPT on the Development of Vocabulary Knowledge of Saudi EFL Students
Ibrahim Mugableh, A. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.18 · Cited by 27
Seth, I., Lim, B., Cevik, J., Sofiadellis, F., Ross, R. J., Cuomo, R., & Rozen, W. M. (2024) · European Journal of Plastic Surgery · doi:10.1007/s00238-024-02162-9 · Cited by 27
Impact of ChatGPT on the Academic Writing Quality of Senior High School Students
Maghamil, M. C., & Sieras, S. G. (2024) · Journal of English Language Teaching and Applied Linguistics · doi:10.32996/jeltal.2024.6.2.14 · Cited by 26
Rapti, S., Sapounidis, T., & Tselegkaridis, S. (2023) · Information · doi:10.3390/info14100530 · Cited by 25
Qian, T., & Zheng, X. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12884 · Cited by 25
Zhang, Y., Liang, Y., Tian, X., & Yu, X. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-023-10339-5 · Cited by 24
Selem, K. M., Saghier, E. G., Khreis, S. H. A., & Tan, C. C. (2025) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2025.2456638 · Cited by 24
CHATGPT IN TEACHING PHYSICS HANDS-ON EXPERIMENTS IN PRIMARY SCHOOL
Kotsis, K. Τ. (2024) · European Journal of Education Studies · doi:10.46827/ejes.v11i10.5549 · Cited by 23
Integrating ChatGPT into the Inquiry-Based Science Curriculum for Primary Education
Kotsis, K. Τ. (2024) · European Journal of Education and Pedagogy · doi:10.24018/ejedu.2024.5.6.891 · Cited by 20
Prompting Large Language Models to Power Educational Chatbots
Farah, J. C., Ingram, S., Spaenlehauer, B., Lasne, F. K.-L., & Gillet, D. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8385-8_14 · Cited by 18
Villan, F., & P. dos Santos, R. (2023) · Acta Scientiae · doi:10.17648/acta.scientiae.7474 · Cited by 18
Paraphrasing ChatGPT Answers as a Tool to Enhance University Students’ Academic Writing Skills
Emran, A., Mohammed, M., Saeed, H., Keir, M. Y. A., Alani, Z. A., & Ibrahim, F. M. (2024) · 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis61505.2024.10459386 · Cited by 17
Using ChatGPT as a Learning Tool: A Study of Ukrainian Students’ Perceptions
Pavlenko, O., & Syzenko, A. (2024) · Arab World English Journal · doi:10.24093/awej/chatgpt.17 · Cited by 17
Proceedings of the 17th Conference on Pattern Languages of Programs
Kohls, C. (2010) · Cited by 16
Beltozar-Clemente, S., & Díaz-Vega, E. (2024) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v14i6.47127 · Cited by 15
Hu, Y.-H. (2024) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.30191/ets.202407_27(3).tp02 · Cited by 11
AI in Education - Evaluating ChatGPT as a Virtual Teaching Assistant
Agarwal, K. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i04.4484 · Cited by 6
The Impact Of Artificial Intelligence On The Quality Of Saudi Translators' Performance
مناوس, أ. م. أ., & الوصي, ب. ق. (2024) · مجلة الأندلس للعلوم الإنسانية والاجتماعية · doi:10.35781/1637-000-096-006 · Cited by 5
University Students Using ChatGPT in Project-Based Learning
Economides, A. A., & Perifanou, M. A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-73990-3_3 · Cited by 4
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
Tian, J., & Zhang, R. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105725 · Cited by 232
Nasr, N. R., Tu, C., Werner, J., Bauer, T., Yen, C., & Sujo-Montes, L. E. (2025) · Education Sciences · doi:10.3390/educsci15091198 · Cited by 123
Yan, L., Pammer‐Schindler, V., Mills, C., Nguyen, A., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.70000 · Cited by 84
Philippakos, Z. A. T., & Rocconi, L. (2025) · Education Sciences · doi:10.3390/educsci15091186 · Cited by 63
ChatGPT in Education: An Effect in Search of a Cause
Weidlich, J., Gašević, D., Drachsler, H., & Kirschner, P. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70105 · Cited by 52
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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