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Off-the-shelf general AI models (e.g. ChatGPT) vs. Specialized AI systems

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Abstract

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.

Introduction

Constructivist learning theory holds that students need to interact with the environment and mentally construct an understanding of knowledge in order for effective learning to occur (Piaget, 1970; Vygotskij and Hanfmann, 1966). With the development 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). ChatGPT is a newly popular technology that has garnered significant attention. Its powerful knowledge base, rapid feedback, and content generation can engage students and foster interaction with it, suggesting a very high potential for supporting student learning. To explore the effectiveness of ChatGPT in supporting student learning, some researchers have tried to introduce ChatGPT into students' learning practice.

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 significantly improve students' learning performance or learning perception (Escalante et al. 2023; Donald et al. 2024) and can even hinder students' learning performance, learning perception, and higher-order thinking (Niloy et al. 2023; Yang et al. 2025). Thus, there is as yet no unified conclusion on whether ChatGPT can significantly improve students' learning performance, learning perception, and higher-order thinking. Therefore, it is necessary to provide objective and scientific evidence for the overall impact of ChatGPT on student learning.

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). Therefore, this study used the meta-analysis method to analyze the results of 51 (quasi) experimental studies published between November 2022 and February 2025 to clarify the impact of ChatGPT on students' learning performance, learning perception, and higher-order thinking, and the circumstances under which ChatGPT can be used well. Specifically, this study focuses on the following two research questions:

RQ1: How effective is ChatGPT in promoting student learning performance, learning perception, and higher-order thinking?

RQ2: How do study characteristics such as grade level, type of course, learning model, duration, the role played by ChatGPT in teaching and learning, and area of ChatGPT application moderate the impact of ChatGPT?

Literature review

ChatGPT in education

Studies to date have primarily explored the educational applications of ChatGPT in higher, secondary, and primary education. In higher education, research has focused on leveraging ChatGPT to assist students in academic writing (Nugroho et al. 2024; Song and Song, 2023), facilitate the comprehension of complex knowledge concepts (Haindl and Weinberger, 2024; Sun et al. 2024; Zhou and Kim, 2024), and solve complex problems (Küchemann et al. 2023; Urban et al. 2024). At the level of secondary education, scholars have primarily explored the use of ChatGPT to assist students in solving difficult problems (Bitzenbauer, 2023; Levine et al. 2025; Yang et al. 2025) and support knowledge review (Wu et al. 2023). In primary education, studies have explored the use of ChatGPT in addressing students' learning challenges (Kotsis, 2024a, 2024b), assisting in optimizing learning plans (Almohesh, 2024), and generating personalized learning resources (Jauhiainen and Guerra, 2024).

In terms of type of courses, scholars have explored the educational applications of ChatGPT in STEM and related subjects, language and writing skills, and skill training courses. In STEM subjects such as mathematics, science, and physics, researchers have explored how ChatGPT can assist students by explaining complex principles and concepts (Bitzenbauer, 2023; Leite, 2023) and providing problem-solving solutions (Sun et al. 2024). In language learning and writing courses, studies have explored the potential of ChatGPT for offering personalized suggestions for text revision (Polakova and Ivenz, 2024; Tsai et al. 2024), inspiring students' writing creativity (Nugroho et al. 2024), engaging students in spoken language practice (Young and Shishido, 2023), and helping students organize outlines for term papers (Su et al. 2023). In skill training courses, scholars have investigated ChatGPT's applications in simulating real-world scenarios (Lu et al. 2024) and collaborating with students to complete complex tasks (Darmawansah et al. 2024; Urban et al. 2024).

In terms of learning models, researchers have mainly explored the use of ChatGPT in supporting personalized learning, problem-based learning, and project-based learning. In personalized learning, studies have explored the potential of ChatGPT for providing targeted guidance to students (Almohesh, 2024; Gouia-Zarrad and Gunn, 2024), developing individualized learning plans (Lin, 2024), and assisting students in reviewing their assignments (Rahimi et al. 2025). In problem-based learning, scholars have investigated the use of ChatGPT for creating learning problem scenarios (Alsharif et al. 2024; Divito et al. 2024; Hui et al. 2025) and offering problem-solving solutions to students (Hamid et al. 2023; Urban et al. 2024). In project-based learning, studies have explored ChatGPT's applications in assisting students with designing, constructing, and completing projects (Avsheniuk et al. 2024; Economides and Perifanou, 2024; Villan and Santos, 2023).

With regard to role-playing, scholars have primarily explored the applications of ChatGPT as an intelligent tutor, an intelligent partner, and an intelligent learning tool. As an intelligent tutor, studies have examined the role ChatGPT can play in delivering knowledge to students (Ba et al. 2024; Ding et al. 2023) and assessing and grading student assignments (Boudouaia et al. 2024; Fokides and Peristeraki, 2025). As an intelligent partner, research to date has primarily focused on ChatGPT's application as a conversational practice robot (Xiao and Zhi, 2023; Young and Shishido, 2023). As an intelligent learning tool, scholars have investigated ChatGPT's role in assisting students with learning tasks (Su et al., 2023), quickly retrieving information (Pavlenko and Syzenko, 2024), and generating personalized learning content (Saleem et al. 2024).

Researchers have also explored the applications of ChatGPT in assessment, tutoring, and personalized recommendations. 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). Regarding tutoring, scholars have explored the use of ChatGPT in addressing students' learning challenges during class (Huesca et al. 2024; Sun et al. 2024) and expanding their extracurricular knowledge (Ba et al. 2024). Regarding personalized recommendations, studies have explored the role of ChatGPT in developing individualized learning plans for students (Albdrani and Al-Shargabi, 2023; Looi and Jia, 2025) and recommending diverse learning resources based on students' needs (Hartley et al. 2024; Jauhiainen and Guerra, 2024).

Empirical studies of ChatGPT on students' learning performance, learning perception, and higher-order thinking

Although many studies have examined the use of ChatGPT in the field of education, as of yet no consensus has been reached on whether it effectively supports students' learning performance, learning perception, and higher-order thinking. Some scholars argue for its benefits (Boudouaia et al. 2024; Gan et al. 2024; Zhou and Kim, 2024). For example, Emran et al. ( 2024) found that adopting ChatGPT as an assistive tool in an undergraduate academic writing skills course effectively improved the students' learning performance. Lu et al. ( 2024) used ChatGPT as an aid in a teacher training program and found that students aided by ChatGPT scored significantly higher on higher-order thinking than those in the control group who were only exposed to traditional teaching methods. Wu et al. ( 2023) conducted a quasi-experimental study in a high school mathematics course: the experimental group used ChatGPT for blended learning, while the control group used only iPads for blended learning. The results indicated that the experimental group significantly outperformed the control group in terms of intrinsic motivation, emotional engagement, and self-efficacy.

Researchers thus do not agree on whether ChatGPT is effective in promoting students' learning performance, learning perception, and higher-order thinking. As the research continues to deepen, researchers have also begun to explore the effectiveness of ChatGPT on student learning using literature reviews and meta-analyses.

Reviews and meta-analyses of ChatGPT and student learning

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). Many of these reviews have reported both positive and negative impacts of ChatGPT on student learning. Regarding the positive impacts of ChatGPT, Ali et al. ( 2024) conducted a systematic review of 112 academic articles to explore the potential benefits and challenges of using ChatGPT in educational contexts. Their findings showed that ChatGPT can enhance student engagement, improve the accessibility of learning resources, strengthen language skills, and assist students in generating text. The study by Zhang and Tur ( 2024) focused on K-12 education to investigate the effects of ChatGPT on student learning. They found that ChatGPT provides learning support across different subjects, enhances personalized learning experiences, and improves learning efficiency. Moreover, Abu Khurma et al. ( 2024) conducted a systematic review of 16 related studies using the PRISMA method to analyze ChatGPT's role in promoting student engagement. They found that ChatGPT meets students' unique learning needs, fosters autonomy and motivation, and helps students gain a deeper understanding of content knowledge. Mai et al. ( 2024) conducted a SWOT analysis based on a systematic review and found that ChatGPT supports students in applying their knowledge to real-life situations and solving practical problems. Imran and Almusharraf ( 2023) explored ChatGPT's role in writing classes in higher education through a systematic review and concluded that ChatGPT improves writing efficiency, sparks creativity, aids translation, enhances content accuracy, and facilitates collaboration.

However, ChatGPT has also been found to have a negative impact on student learning. Ali et al. ( 2024) found that ChatGPT in education still has several shortcomings, such as generating incorrect answers, triggering academic plagiarism, and causing students to become dependent on technology. Zhang and Tur ( 2024) found that ChatGPT can reduce students' abilities, decrease teacher-student interaction, and mislead students with incorrect information. Abu Khurma et al. ( 2024) found that ChatGPT tends to make students overly reliant on technology, and can potentially provide inaccurate or biased information that can lead to cognitive biases and hinder the development of students' critical thinking skills. 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. Lo et al. ( 2024) concluded that the use of ChatGPT can lead to plagiarism and academic misconduct, can provide incorrect information that can lower students' emotional attitudes toward learning, and can weaken students' independent thinking skills if they become excessively reliant on ChatGPT. Mai et al. ( 2024) argued that first-time users may find it challenging to identify inaccurate or misleading information generated by ChatGPT, adding to their cognitive load. Imran and Almusharraf ( 2023) pointed out that ChatGPT suffers from delayed information updates that may lead to harmful or incorrect content, which can mislead students and instill cognitive biases.

Although these literature reviews provide valuable insights into the application of ChatGPT in education, most of the studies they cover have been primarily theoretical, making it difficult to establish a causal relationship between ChatGPT use and student learning outcomes or to identify key moderating factors. This may explain the discrepancies in the conclusions drawn from different reviews. For example, Lo et al. ( 2024) argued that ChatGPT can either hinder or facilitate the development of students' critical thinking, while Abu Khurma et al. ( 2024) argued that ChatGPT only harms students' critical thinking. Therefore, it is essential to further analyze the actual impact of ChatGPT on student learning.

Some scholars have addressed this issue by conducting meta-analyses of experimental studies to search for the causal relationship between ChatGPT use and student learning. Heung and Chiu ( 2025), for example, conducted a meta-analysis to examine the impact of ChatGPT on student engagement. Their results indicate that ChatGPT can effectively enhance students' behavioral, cognitive, and emotional engagement in learning activities. Their study thus provides valuable insights into the impact of ChatGPT on student engagement. However, the scope of their study was somewhat narrow, as it focused solely on student engagement in learning and did not include any moderating variables, which limits their comprehensive analysis of the overall effectiveness of ChatGPT. Guan et al. ( 2024) focused on informal digital learning English (IDLE) and investigated the impact of ChatGPT on students' English proficiency, learning motivation, and self-regulation abilities. Their analysis revealed that ChatGPT use in IDLE contexts significantly contributes to enhancing English proficiency and self-regulation but has no significant effect on learning motivation. Again, their study makes a significant contribution to understanding the effectiveness of ChatGPT in the context of English learning. However, their meta-analysis was confined to a single domain, with a relatively narrow scope of focus. Deng et al. ( 2025) conducted a meta-analysis of 62 studies to explore the impact of ChatGPT on student learning. They found that ChatGPT can effectively enhance students' learning performance, improve the emotional-motivational aspects of learning, foster higher-order thinking, boost self-efficacy, and significantly reduce cognitive load. Their study greatly advances our understanding of the impact of ChatGPT on student learning, but there are some issues with the included samples. First, the samples selected for such a study should reflect students' actual learning after using ChatGPT as a learning aid. Simply using ChatGPT as a general tool without any learning process involved cannot adequately demonstrate its impact on student learning. For example, using ChatGPT solely for translation tasks (e.g., Moneus and Al-Wasy ( 2024) does not provide evidence for its influence on student learning. Additionally, ChatGPT itself does not directly affect student performance; the way in which the technology is applied is crucial to its effect. Therefore, both the experimental and control groups should adopt the same learning or teaching method to ensure comparability. However, in one of the sample studies (Beltozar-Clemente and Díaz-Vega, 2024) included by Deng et al. ( 2025), the experimental group received an intervention combining gamified learning with ChatGPT, but it was not specified whether the control group also used gamified learning. This omission may compromise the rigor of the study. While the findings of Deng et al. ( 2025) offer some reference value, their conclusions are insufficiently robust. Therefore, a more comprehensive meta-analysis is still needed to provide reliable results regarding the impacts of ChatGPT and the variables that modify its effects on students' learning performance, learning perception, and higher-order thinking.

Method

Research design

Examination of prior studies (Abuhassna and Alnawajha, 2023a, 2023b; Abuhassna et al. 2023; Masrom et al. 2021; Samsul et al. 2023) shows that most systematic literature reviews have been conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a well-known standard for systematic evaluation in many fields (Abuhassna and Alnawajha, 2023b). Therefore, this meta-analysis selected studies using the PRISMA guidelines and selection procedures (Moher et al. 2009; Page et al. 2021), as doing so can increase the transparency of the meta-analyses and ultimately improve the quality and reproducibility of the research (Page and Moher, 2017). The analysis in this study comprised four main steps: (1) conducting a literature search, (2) establishing inclusion and exclusion criteria, (3) coding study characteristics, and (4) performing data analysis.

Literature review process

The query search ("ChatGPT" OR "chat generative pre-trained transformer" OR "GPT-3.5" OR "GPT-4" OR "GPT-4o" OR "GenAI" OR "generative AI" OR "generative artificial intelligence" OR "Artificial Intelligence Generated Content" OR "AIGC") AND ("educat*" OR "learn*" OR "teach*") AND ("experiment*" OR "randomised controlled trial*" OR "randomised controlled trial*" OR "RCT*" OR "quasi-experiment*" OR "intervention") was conducted across multiple databases including the Web of Science Core Collection, Elsevier Science Direct, Taylor & Francis, Wiley Online Library, and Scopus. Since ChatGPT was launched by OpenAI in November 2022, the search period was restricted from November 2022 to February 2025. Initially, a total of 6,621 articles were obtained through database searches.

Inclusion and exclusion criteria

The literature inclusion and exclusion criteria were developed by two researchers specializing in educational technology, and are presented in Table 1.

After excluding duplicate articles ( n = 268) and non-English publications ( n = 20), samples were further screened by reviewing titles and abstracts to exclude studies unrelated to the impact of ChatGPT on student learning. Studies included in the review were required to investigate how ChatGPT affects student performance, learning perception, or higher-order thinking. Any study not meeting this criterion was excluded (e.g., research exploring factors influencing students' intention to use ChatGPT (Yu et al. 2024)). Studies that did not involve the learning process were also excluded (e.g., a study that merely used ChatGPT as a translation tool rather than as a way to enhance translation skills (Moneus and Al-Wasy, 2024)). At this stage, 3433 studies were removed. The meta-analysis of this study focuses specifically on ChatGPT. Thus, studies using other AI tools were excluded, such as Seth et al. ( 2024). This process resulted in the exclusion of an additional 126 studies, bringing the total to 3559 studies removed in this stage.

Subsequently, a thorough review of the remaining 2712 articles was conducted. First, studies that did not use quasi-experimental or experimental designs were excluded. To be included, experimental studies had to include at least one experimental group and one control group and employ methods such as random assignment, statistical matching, or controlling for pretest differences. Therefore, studies with non-experimental or non-quasi-experimental designs were excluded, such as Jošt et al. ( 2024). At this stage, 1455 articles were removed.

This study used comprehensive meta-analysis software (version 3.3.0) to calculate the standardized mean difference Hedges's g for the effect of ChatGPT on student learning performance, learning perception, and higher-order thinking. The calculation was based on either: (a) the sample sizes, means, and standard deviations of the post-test data of the experimental and control groups, or (b) the sample sizes, means, and standard deviations of the pre-test and post-test data from a single experiment. When means or standard deviations were unavailable, χ 2, t, and F values were used instead. The included samples were thus required to report at least the sample size, mean, and standard deviation, or else the χ 2, t, and F values. Samples with insufficient data to compute Hedges's g were excluded as well. At this stage, 17 articles were removed.

Articles that met the coding criteria were independently selected by two researchers, and acceptable interrater reliability was achieved (Cronbach's α = 0.96). Any disagreements were resolved through consensus reached via repeated discussion between the researchers. The final sample consisted of 51 articles, published between November 2022 and February 2025. Figure 1 illustrates the selection process (see Supplementary Fig. 1 PRISMA).

Coding of study characteristics

To analyze the impact of ChatGPT on student learning performance, learning perception, and higher-order thinking, a specific coding scheme was needed for a comprehensive analysis of the 51 selected articles. This scheme was meticulously developed by thoroughly examining the included literature and drawing on insights by Zheng et al. ( 2021). A total of six moderating variables were selected (see Table 2).

The coding of moderating variables followed the scheme outlined in Table 2. The data within the articles were coded according to the following criteria: (a) The effect sizes for each independent sample in the study were recorded. (b) When a study included two or more experiments, each experiment was counted as one effect size. (c) When a study reported data on different dimensions of the same variable (e.g., dividing higher-order thinking into critical thinking and creative thinking, and reporting separate scores for each), the average effect size across all dimensions was coded. Ultimately, the coding process resulted in the extraction of 72 independent effect sizes from the selected 51 articles.

Data analysis

Comprehensive Meta-Analysis 3.0 (CMA3.0) software was used to process the collected data. Following the example of Lipsey and Wilson ( 2001), effect sizes and variances were extracted from each study. The scores of two or more groups for each condition were averaged before calculating the effect size. Most of the included studies reported the experimental and control groups' post-test means and standard deviations, and these were used to calculate the standardized mean difference effect sizes. 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 calculated effect sizes were interpreted according to guidelines proposed by Sawilowsky ( 2009): very small (0.1), small (0.2), medium (0.5), large (0.8), very large (1.2), and huge (2.0).

Results

Table 3 presents the 72 effects obtained from the coding of the 51 studies, including the sample size ( N), independent effect size ( g), grade level, type of course, duration, learning model, role of ChatGPT, area of ChatGPT application, and learning effects.

Heterogeneity

This study used the Q-test and I 2 to calculate the level of heterogeneity within the sample. Cochran's Q mainly examines the p-value, with a value less than 0.1 indicating heterogeneity and that the random effects model should be used for meta-analysis (Zhang et al., 2024). I 2 assesses the degree of heterogeneity, with 0-25% indicating low heterogeneity, 25-75% indicating moderate heterogeneity, and 75-100% indicating high heterogeneity (Higgins et al., 2003). According to Table 4, the Q-value 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 Q-value for learning perception is 65.646, and the p-value of the Q-test is less than 0.1. The I² value is 72.580%, indicating a moderate degree of heterogeneity in the included studies regarding the effect of ChatGPT on learning perception. The Q-test for higher-order thinking is 18.105, and the p-value of the Q-test is less than 0.1. The I ² value is 55.813%, indicating moderate heterogeneity in the included studies regarding the effect of ChatGPT on higher-order thinking. Consequently, the random effects model was selected for the analysis of learning performance, learning perception, and higher-order thinking.

Publication bias and sensitivity analysis

The funnel plot and fail-safe N test were used to assess potential publication bias. In the absence of publication bias, a funnel plot should resemble a symmetrical inverted funnel, whereas an asymmetrical or incomplete funnel indicates the possibility of bias (Schulz et al. 1995). The funnel plots for the present research are shown in Supplementary Figs. 2 through 4 (see Supplementary Fig. 2 Funnel plot for learning performance, Supplementary Fig. 3 Funnel plot for learning perception, and Supplementary Fig. 4 Funnel plot for higher-order thinking). 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. However, some points are outside the two diagonal lines, indicating potential bias or heterogeneity in the included studies, possibly due to differences in sample size or research design (Figs. 2- 4).

Meanwhile, if the calculated fail-safe N is greater than the tolerance value of 5 k + 10, where k is the sample size, the estimated effect size of an unpublished study is considered unlikely to influence the overall effect size of the meta-analysis (Borenstein et al. 2009). In this study, the fail-safe N for learning performance was calculated to be 5490 (5 k + 10 = 230, k = 44), for learning perception was calculated to be 399 (5 k + 10 = 105, k = 19), and for higher-order thinking was calculated to be 101 (5 k + 10 = 55, k  = 9), indicating that the possibility of publication bias is relatively small for this study.

Because some points lay outside the two diagonal lines, a sensitivity analysis was conducted to further assess the robustness of the meta-analysis results: this technique is used to examine outliers that might affect the magnitude of the overall effect. One-Study-Removal Analysis was used to detect the impact of extreme positive and negative effects on the overall effect. 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 fixed-effects model and between 0.683 and 1.052 for the random-effects model. For learning perception, after the removal of any single study, the range of the 95% CI of the effect size remained between 0.235 and 0.387 for the fixed-effects model and between 0.287 and 0.625 for the random-effects model. For higher-order thinking, after the removal of any single study, the range of the 95% CI of the effect size remained between 0.266 and 0.509 for the fixed-effects model and between 0.255 and 0.650 for the random-effects model. Thus, the meta-analysis results obtained in this study were demonstrated to be very stable.

Overall effectiveness

The results of the meta-analysis are presented in Table 4. 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 effects of the moderating variables

Separate moderator analyses were conducted for learning performance, learning perception, and higher-order thinking to explore possible causes of heterogeneity.

Table 5 presents the results of the moderator analysis for learning performance. Significant differences were observed among the types of courses ( Q B = 64.249, p < 0.001), with skills and competencies development ( g = 0.874) showing the strongest effect. Learning models also exhibited significant differences ( Q B = 76.220 p < 0.001), primarily because ChatGPT had a larger effect in problem-based learning ( g = 1.113), than in other learning models. Similarly, duration showed significant differences ( Q B = 55.998, p < 0.001), with ChatGPT demonstrating a large effect in interventions lasting 4-8 weeks ( g  = 0.999).

No significant differences were found for grade level ( Q B = 0.627 p = 0.428), roles of ChatGPT ( Q B = 6.261, p = 0.100), and area of ChatGPT application ( Q B = 6.210, p  = 0.102). 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.

Table 6 presents the results of the moderator analysis for learning perception. Of all the tested moderating variables, intervention duration was the only one to have a significant effect ( Q B = 19.839, p < 0.001). 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). No significant differences were observed for grade level ( Q B = 0.360, p = 0.562), type of courses ( Q B = 1.338, p = 0.512), learning models ( Q B = 5.752, p = 0.124), role of ChatGPT ( Q B = 2.748, p = 0.253), or area of ChatGPT application ( Q B = 4.861, p  = 0.088).

Table 7 presents the results of the moderator analysis for higher-order thinking. All relevant studies were conducted at the college level, and thus no analysis was performed for grade level. Significant differences were observed for type of courses ( Q B = 7.811, p < 0.05). Specifically, ChatGPT had a large positive effect on higher-order thinking for STEM and related courses ( g = 0.737), a nearly medium positive effect for language learning and academic writing ( g = 0.334), and a small positive effect for skills and competencies development ( g  = 0.296).

There were significant differences for roles of ChatGPT as well ( Q B = 4.872, p < 0.05). ChatGPT was most effective when acting as an intelligent tutor ( g  = 0.945), having the largest impact on higher-order thinking. Mixed roles and intelligent partners were excluded from the analysis due to a lack of sufficient data, as each was represented by only one study.

No significant moderating effects were detected for the learning model ( Q B = 4.109, p = 0.128), duration ( Q B = 2.988, p = 0.224), or application area ( Q B = 5.073, p  = 0.079).

Discussion

How effective is ChatGPT in promoting learning performance?

The results indicate that ChatGPT has a substantial positive effect on students' learning performance, with an overall effect size of 0.867. The meta-analysis further revealed that the impact of ChatGPT on students' learning performance is moderated by three variables: type of course, learning models, and intervention duration. The overall effect size indicated that ChatGPT is effective in improving students' learning performance, a result that is consistent not only with prior meta-analyses (Deng et al. 2025) but also with the findings of most empirical studies (Alneyadi and Wardat, 2023; An et al. 2025; Darmawansah et al. 2024). Deng et al. ( 2025) argued that ChatGPT may genuinely enhance learning performance by enabling personalized learning experiences, providing immediate access to information and diverse perspectives, and allowing students to engage more deeply with the material. A quasi-experimental study by Alneyadi and Wardat ( 2023) showed that students who incorporated ChatGPT into their studies tended to acquire additional knowledge, thereby improving their understanding of complex concepts and enhancing their learning performance. Interestingly, the current study compares the results of the present meta-analysis with those of another meta-analysis that focused on the effects of AI-based assessment tools on learners' learning performance (Chen et al. 2025). 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). This may be because the capabilities of traditional AI assessment technologies are relatively limited (Chen et al. 2025), in that they are primarily confined to evaluation tasks. In contrast, as an emerging form of generative AI, ChatGPT offers a wider range of functionalities, such as text generation and interactive communication (Gallent-Torres et al. 2023). These features enable ChatGPT to support more diverse learning scenarios, which may, in turn, contribute to its larger effect size.

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). The most significant impact was observed for courses focused on skills and competencies development. This may be because such courses typically involve well-defined task objectives and procedural steps; ChatGPT provides immediate feedback, targeted guidance, and problem-solving support (Rakap, 2023; Urban et al. 2024), and thus can enable students to complete such tasks more efficiently.

The present study reveals how students' learning patterns influence the positive impact of ChatGPT on their academic performance. Specifically, of the various learning models, ChatGPT demonstrates the most significant effect when applied to problem-based learning. 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). ChatGPT thus allows students to access ample information that can enable them to solve teacher-assigned problems more efficiently, ultimately enhancing their learning performance. Moreover, 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. This may be because project-based learning emphasizes the completion of a comprehensive project in a real-world 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.

How effective is ChatGPT in improving learning perception?

Our results show that ChatGPT has a moderately positive effect on students' learning perceptions, with an overall effect size of 0.456. In addition, the impact of ChatGPT on students' learning perceptions is moderated by intervention duration. These meta-analytic findings show that ChatGPT is effective in fostering students' emotional attitudes toward learning, consistent with the results of prior studies (Chen and Chang, 2024; Lu et al. 2024; Urban et al. 2024). The technology acceptance model posits that perceived usefulness and perceived ease of use influence an individual's intention or attitude toward using new technology (Venkatesh and Bala, 2008). Therefore, the perceived usefulness and perceived ease of use of ChatGPT determine whether students will adopt it. ChatGPT can make a course more interesting and encourage students to actively search for information, ultimately contributing to an increase in learning perception. ChatGPT can meet student learning needs through actions such as providing instant feedback, offering personalized scaffolding of learning, and provoking interesting learning experiences (Li, 2023). Therefore, the students' learning perception is significantly improved. Notably, compared to its large positive impact on improving learning performance ( g = 0.867), ChatGPT has only a moderately positive impact on learning perception. This may be because, although ChatGPT can quickly provide knowledge, answer questions, and generate content to enhance students' academic performance, it lacks emotional intelligence (Birenbaum, 2023), limiting its ability to provide the kind of "humanized" interaction needed to establish emotional resonance with students or spark deeper learning interest (Park and Ahn, 2024). Therefore, when guiding students to use ChatGPT as a learning aid, teachers should pay attention to their students' emotional and psychological needs to avoid overly mechanized and tool-driven learning experiences.

Of the six moderating variables examined, only intervention duration showed a significant effect ( p < 0.05). More specifically, student learning perception increased with prolonged use of ChatGPT. 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. Such feedback likely reinforces their positive emotions toward learning over time.

How effective is ChatGPT in fostering higher-order thinking?

Our results indicate that ChatGPT has a moderately positive effect on the development of students' higher-order thinking, with an overall effect size of 0.457. Moreover, the effect of ChatGPT on higher-order thinking is moderated by the type of course and the role played by ChatGPT in the learning process. These meta-analytic findings suggest that ChatGPT can effectively foster students' higher-order thinking skills, in agreement with the results of prior studies (Li, 2023; Lu et al. 2024; Niloy et al. 2023). Lu et al. ( 2024) found that ChatGPT was able to help students reflect on their learning processes and strategies by providing immediate feedback and assessment, and could also help them formulate problem-solving ideas to enhance their metacognitive thinking and problem-solving skills. Woo et al. ( 2023) suggested that this could be attributed to ChatGPT's ability to present students with diverse information, thus promoting divergent and creative thinking. Metacognitive thinking, problem-solving abilities, and creative thinking are all crucial components of higher-order thinking. 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. This may be because ChatGPT can use simple language to express complex information (Niloy et al. 2023) and can provide students with a structured and logical framework for new knowledge (Li et al. 2024). This helps them master conceptual knowledge, and thus can rapidly improve students' learning performance. However, ChatGPT is based on pretrained models that rely on existing data and patterns. It thus may lack critical analysis abilities and the ability to provide creative problem-solving solutions (Lu et al. 2024). Moreover, ChatGPT-generated content is not always completely accurate, and this bias can hinder the development of students' higher-order thinking (Niloy et al. 2023), thus limiting the overall degree to which ChatGPT can promote students' higher-order thinking.

The analysis of potential moderating variables shows that ChatGPT's effectiveness in fostering higher-order thinking differs significantly across different types of courses. Specifically, ChatGPT has the most substantial impact when used in STEM and related courses. This may be due to the goals of these courses. 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.

In terms of the roles that ChatGPT can play, its function as an intelligent tutor has a particularly strong impact on the development of students' higher-order thinking. When acting as an intelligent tutor, ChatGPT provides students with personalized guidance, feedback, and assessment (Escalante et al. 2023). 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' higher-order thinking skills (Lu et al. 2024).

Conclusion and implications

Conclusion

This study used meta-analysis to analyze the impact of ChatGPT on student learning performance, learning perception, and higher-order thinking. With regard to learning performance, an analysis of data from 44 experimental and quasi-experimental studies showed that the calculated effect size indicates a large positive impact ( g  = 0.867) of ChatGPT on student learning performance. The analysis of moderating variables revealed no significant differences in the effect of ChatGPT across grade levels, role of ChatGPT, or area of ChatGPT application. However, significant differences were found in the type of course, learning model, and duration.

For learning perception, an analysis of data from 19 experimental and quasi-experimental studies revealed a medium positive effect ( g  = 0.456) of ChatGPT on student learning perception. The moderator analysis indicated that there were no significant differences in its impact across grade level, type of course, learning model, role of ChatGPT, or area of ChatGPT application. However, duration showed a significant effect.

In terms of higher-order thinking, data from nine experimental and quasi-experimental studies indicated a medium positive effect ( g  = 0.457) of ChatGPT on students' higher-order thinking. The analysis of moderating variables revealed no significant differences in the effect of ChatGPT across the learning model, duration, or area of the ChatGPT application. However, significant differences were observed in the type of course and role of ChatGPT.

In summary, the meta-analysis results of this study confirm the positive impacts of ChatGPT on learning performance, learning perception, and higher-order thinking, while highlighting some variations in effectiveness across different usage conditions and learning contexts. Based on these results, governments could introduce policies to promote the integration of ChatGPT into education, encouraging universities and secondary schools to apply ChatGPT appropriately. Additionally, ChatGPT could be actively incorporated into STEM and related courses, skills and competencies development training programs, and language learning and academic writing courses to enhance student learning.

Implications for theory and practice

This meta-analysis makes significant contributions to our understanding of the theoretical and practical aspects of the use of ChatGPT in education. From a theoretical perspective, it extends the existing literature on ChatGPT and student learning by providing quantitative evidence that ChatGPT can indeed effectively improve learning performance, enhance learning perception, and foster higher-order thinking. Second, this study provides a more thorough and integrated investigation of potential moderating variables. It explores the effects of ChatGPT on student learning across different types of courses, grade levels, learning models, intervention durations, instructional roles, and application types. It has a broader scope than the meta-analyses by Heung and Chiu ( 2025) and Guan et al. ( 2024). Compared to Deng et al. ( 2025), this study further explores learning methods, the role of ChatGPT in learning, and its application across various domains. Its findings reveal that different learning methods and the roles played by ChatGPT significantly affect students' learning performance and higher-order thinking, resulting in notable differences in outcomes. Broadly speaking, this study provides a theoretical foundation for the future development of national AI education policies and the application of ChatGPT by educators, while further enriching the research on the use of ChatGPT in the field of education.

From a practical perspective, the results of the analysis of potential moderating variables in this study show that ChatGPT should not be implemented arbitrarily. Instead, it should be used scientifically and reasonably according to the type of course, learning model, duration, etc. Future applications of ChatGPT should pay attention to the following points: (1) When applying ChatGPT to developing students' higher-order thinking, it is crucial to provide corresponding learning scaffolds or educational frameworks (e.g., Bloom's taxonomy) because ChatGPT lacks creativity and critical thinking. Doing so can improve its effectiveness in cultivating higher-order thinking. (2) Broad cross-grade use should be encouraged to support student learning. ChatGPT can serve as an auxiliary learning tool for both middle school and university students, enhancing both their academic performance and their learning perception. In middle schools, teachers can use ChatGPT to maintain students' interest while improving their understanding and memory of complex concepts. In universities, teachers can guide students to actively use ChatGPT in relevant courses to provide personalized learning suggestions to improve their learning. (3) ChatGPT can be actively applied in different types of courses to enhance student learning. For example, in STEM and related courses, ChatGPT can be used to present diverse information to help students think divergently and complete projects. Alternatively, ChatGPT can be used to simulate problem-based learning environments by generating scenario-based questions. In skills and competencies development training, ChatGPT can be used to provide more targeted guidance. In language learning and academic writing courses, ChatGPT can help students enhance their comprehension of texts and improve the accuracy and grammatical correctness of their writing. (4) ChatGPT can be applied in different learning models, particularly in problem-based learning, as its use in this mode best supports students' learning performance. (5) Continuous use of ChatGPT can be ensured to support student learning, with a recommended duration of four to eight weeks. For shorter durations, teachers should provide relevant scaffolding, such as guidance on how to write high-quality ChatGPT prompts. If a longer duration is necessary, teachers can consider introducing new instructional models, such as blended learning or flipped classrooms, and apply ChatGPT more strategically in different teaching stages such as pre-class preparation, in-class interaction, or post-class review to enhance learning outcomes. Moreover, teachers can conduct regular formative assessments to monitor students' progress and identify gaps in their understanding. This would help prevent the students from developing an over-reliance on technology that could hinder their knowledge retention, while also promoting deeper learning and long-term mastery. (6) ChatGPT should be used flexibly in teaching as an intelligent tutor, partner, learning tool, and more. Its role(s) can be adjusted according to the requirements of the teaching and learning activities to which it is applied.

Limitations and future research

There are several limitations that should be solved for future work. First, this study sample size was limited, comprising only 51 documents, of which only one addressed primary school and none addressed kindergarten. 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. Therefore, future work could include more studies. 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. Future studies should address a broader scope of factors. Finally, this study only selected experimental and quasi-experimental studies and did not include relevant qualitative or mixed-methods research. Future review studies could integrate a broader array of related studies, including qualitative and mixed-methods research, to analyze the impact of ChatGPT on student learning from a more comprehensive and holistic perspective, thereby broadening the scope and applicability of the findings.

Acknowledgement

This work was supported by the Major Project of Humanities and Social Sciences Key Research Base of the Ministry of Education (Grant No. 22JJD880023), University-Industry Collaborative Education Program (Grant No. 240904171111322), Hangzhou Social Science Routine Planning Project (Grant No. Z24JC014) and Hangzhou Normal University's 2023 Teaching Construction and Reform Project "Exploration and Practice of Generative Artificial Intelligence to Promote University Teaching Reform".

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Authors and Affiliations

Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China

Jin Wang & Wenxiang Fan

Chinese Education Modernization Research Institute of Hangzhou Normal University (Zhejiang Provincial Key Think Tank), Hangzhou, China

Wenxiang Fan

Authors

Jin Wang

Wenxiang Fan

Contributions

JW designed the research plan, was responsible for coding studies, experimental design, and data collection, participated in data analysis and interpretation, as well as writing and revising the initial manuscript. WF proposed the research topic, played an important guiding role throughout the research project, and made significant contributions to manuscript writing, revision, review, and polishing.

Corresponding author

Correspondence to Wenxiang Fan.

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Wang, J., Fan, W. The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanit Soc Sci Commun 12, 621 (2025). https://doi.org/10.1057/s41599-025-04787-y
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The effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking: insights from a meta-analysis - Humanities and Social Sciences Communications

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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.

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Artificial intelligence-assisted academic writing: recommendations for ethical use - Advances in Simulation

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Generative artificial intelligence (AI) tools have been selectively adopted across the academic community to help researchers complete tasks in a more efficient manner. The widespread release of the Chat Generative Pre-trained Transformer (ChatGPT) platform in 2022 has made these tools more accessible to scholars around the world. Despite their tremendous potential, studies have uncovered that large language model (LLM)-based generative AI tools have issues with plagiarism, AI hallucinations, and inaccurate or fabricated references. This raises legitimate concern about the utility, accuracy, and integrity of AI when used to write academic manuscripts. Currently, there is little clear guidance for healthcare simulation scholars outlining the ways that generative AI could be used to legitimately support the production of academic literature. In this paper, we discuss how widely available, LLM-powered generative AI tools (e.g. ChatGPT) can help in the academic writing process. We first explore how academic publishers are positioning the use of generative AI tools and then describe potential issues with using these tools in the academic writing process. Finally, we discuss three categories of specific ways generative AI tools can be used in an ethically sound manner and offer four key principles that can help guide researchers to produce high-quality research outputs with the highest of academic integrity.

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The rapid acceleration of artificial intelligence (AI) is transforming our world, and the emergence of chatbots like ChatGPT, and the integration of AI into Microsoft with Co-Pilot and others is a testament to this revolution. As we witness these groundbreaking advancements, schools have a critical role to play in preparing learners for a future that is already here.

As AI rapidly advances and becomes an integral part of our daily lives, we must adapt our educational systems to foster the skills and mindsets required to thrive in this new landscape. Now, more than ever, we need to embrace change and adopt innovative problem-solving approaches like design thinking, to reshape education and unlock the full potential of our learners with the skills that make us uniquely human.

While using ChatGPT can be great for writing emails, and automating our mundane tasks, it presents a larger opportunity to have a thought partner to solve challenges with, and design strategies for turning ideas into impact.

In this post, I share a framework called SPARK for designing effective prompts with ChatGPT and other generative AI tools.

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When thinking about your role, what do you want to be known for?

What you hear people respond with are the qualities that make us uniquely human. Traits like being a mentor, being reliable, being mission-driven as a leader, and being a role model for the values of the organization.

This question was inspired by the book Atomic Habits. And it has been an incredibly powerful question that sparks a connection in the audience as people reconnect and reflect on who they want to be. This has become my favorite question to ask any group I'm working with. And I encourage you to ask this as well. I have found that this subtle reminder of what makes us uniquely human creates an optimistic and enthusiastic environment when discussing AI technologies.

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These moments of sharing our hopes, dreams, and fears connect us and make us uniquely human. When we ask people what is holding them back, I am always in awe of the vulnerability with which people share. It's these curiosities that can help us SPARK the use of AI. To help you do this I created a framework you can use to apply design thinking when using ChatGPT to turn your obstacles into opportunities.

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The SPARK framework was designed to help people reconnect with their hopes and dreams. Individuals and teams can use this to guide them through the process of identifying and solving problems by creating a clear, concise, and compelling narrative around a specific issue or challenge.
Note:
Always begin by confirming any method you use with the chatbot. In this example, we will use ChatGPT. This is the case for any method or strategy, always confirm what it knows.

[ START PROMPT] I want you to remember this framework and use it to help me design a solution.

Situation: Define the current situation or context that needs to be addressed.

Problem: Identify the problem or challenge that needs to be solved.

Aspiration: Describe the desired outcome or goal that you want to achieve.

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Tell me when you remember [END PROMPT]

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Here you begin by describing the situation. The same way you onboard a new hire, is the same way you want to onboard your AI. Tell them about your unique context, the more details you input, the more precise your output will be.
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Once we enter the prompt into ChatGPT your assistant gets to work! The key to continuing the conversation is to treat it like your assistant. Scroll through the gallery to see the conversation.

It All Starts with A Human Prompt

With technology this powerful it's a shame to simply use it to substitute tasks like quizzes and grading. While those are important elements, this is an opportunity to step back and really examine what your learners need. What experiences do you wish you could create but simply don't have the time to do?

Integrating the SPARK framework into how you use tools like ChatGPT can help you focus on developing skills. Such as creativity, collaboration, communication, and problem-solving. These skills are the foundation for success in the age of AI. They enable individuals to adapt to new technologies, engage with complex issues, and contribute meaningfully to society. By fostering these skills, we can ensure that students are better prepared to navigate the rapidly changing landscape of the 21st century.

The Future of AI in Education

As we look to the future, the integration of AI, like ChatGPT, into various aspects of our lives will only continue to accelerate. As a society, we must rise to the challenge and seize the opportunity to reshape education. Ensuring that the next generation of students and educators can thrive in this new era.

Let us come together as educators, policymakers, parents, and students to champion the adoption of design thinking in our schools. By doing so, we can dismantle the panopticon-like structures that have stifled innovation and creativity in education for far too long. The future of our world depends on our ability to adapt, learn, and grow. It's time to embrace change and unlock the limitless potential of our students and teachers. Together, we can create a brighter, more prosperous future for all.

If you're interested in learning more about strengthening your AI literacy skills with how you use ChatGPT, and other AI tools to create dynamic and engaging learning experiences I invite you to join our first cohort of The AI Bootcamp, a self-paced course that begins May 17.

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Runway is an applied AI research company shaping the next era of art, entertainment and human creativity.

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GravityWrite: Top AI Writer, Content Generator & Assistant

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Use AI to create high-quality content for blogs, ads, emails, and social media in seconds. Boost your clicks, conversions, and sales with GravityWrite.

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Living guidelines on the responsible use of generative AI in research

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Living guidelines on the responsible use of generative AI in research

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Create stunning original music for free in seconds using AI. Make your own masterpieces, share with friends, and discover music from artists worldwide.

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Talk with Claude, an AI assistant from Anthropic

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An AI chatbot, or Artificial Intelligence chatbot, is a technology that uses natural language processing (NLP) and machine learning algorithms to understand and respond to your questions or commands in real time. It's designed to simulate human-like conversations and interactions.

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Today we’re at Molokini Crater. This pleasant cres

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These models increase image resolution and quality.

Read our guide to upscaling images with AI to learn about various upscaling models.

Key capabilities of upscaling models:

Super-resolution - Upscale images by inferring high frequency details. Allows increasing resolution without losing quality.

Noise reduction - Remove artifacts and imperfections during upscaling. Results in cleaner image.

Face restoration - Improve facial features and textures for more natural faces.

Control over style - Adjust degree of realism vs hallucination during upscaling.

For most upscaling needs, we recommend the batouresearch/magic-image-refiner model. This very flexible model can be used for upscaling, refining an image, or inpainting. The model can upscale images to either 1024x1024px or 2048x2048px, producing stunning results with significant detail.

Increase the resemblance parameter to get a more precise recreation of your original input image. Or, if you're looking for something new and interesting, crank up the creativity parameter to encourage hallucination and create a new image inspired by your original input.

You may also be interested in the sister model, batouresearch/high-resolution-controlnet-tile, which upscales to a larger resolution of 2560x2560. However, it runs slower and produces less realistic-looking results.

If you need to upscale a large volume of images, we suggest using nightmareai/real-esrgan.

It runs fast on cheaper GPUs, like the Nvidia T4 (~1.8s for a 2x upscale), and produces reasonably good upscaled images without too many image scaling artifacts. Real-ESRGAN also includes an optional face_enhance option, which can help improve the quality and realism of AI-generated faces.

You can run Real-ESRGAN on an Nvidia A100 for faster upscaling speed (~0.7s for a 2x upscale) and 2.5x the amount of GPU RAM, allowing for significantly larger images. Just keep in mind that A100 GPUs cost more than T4 GPUs.

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These models increase image resolution and quality. Read our guide to upscaling images with AI to learn about various upscaling models. Key capabilities of upscaling models: Super-resolution - Upscale images by inferring high frequency details. Allows increasing resolution without losing quality. Noise reduction - Remove artifacts and imperfections during upscaling.

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CapCut's video upscaling free AI is powerful as well as versatile. Whether you're dealing with different video formats, contexts, resolutions, or anything else, CapCut's free video upscaler online has got your back covered! From professional projects to personal creations, achieve extraordinary results by processing and optimizing your videos to suit a wide range of needs and contexts.

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Let AI Video Upscaler Take Your Video to the Next Level

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Video upscaler to help you upscale videos for free. Learn how to use AI video upscaling to upscale videos to 4K. CapCut makes that possible for you!

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The idea-to-video platform that sets your creativity in motion.

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Insta 3D is an AI tool that can convert any 2D image into a complete 3D world.

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Trusted by 2+ Million Users!

Turn 2D images into captivating 3D animations using LeiaPix AI

Dive into the extraordinary with LeiaPix, where your everyday 2D images transform into breathtaking 3D masterpieces. Here, innovation meets art, turning memories into immersive experiences.

Every picture opens a portal to a new dimension. Explore the future of imagery today.

Depth Map Generated by AI

Leia Inc. stands at the zenith of 3D technology, driven by a pioneering legacy and an unmatched dataset of hundreds of millions of high-quality 3D pictures.

Control Your Storytelling

Utilize our preset options or customize using advanced settings to create your ideal 3D depth animation.

Looking for a complimentary download of our depth animation? Get your free 720p MP4 file now!

Unveiling LeiaPix's full package download, the epitome of convenience and efficiency for your 3D creations.

Step into the future with LeiaPix API, designed to integrate LeiaPix directly into your existing workflow.

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LeiaPix - Convert 2D to 3D Depth Animation AI Technology

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Transform 2D images into eye-catching 3D animations using our advanced and user-friendly 3D animation software. Join the forefront of 3D technology with LeiaPix.com

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Genie, a research preview of Luma's generative 3d foundation model

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3d text-to-3d generation model luma genie

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3d text-to-3d generation model luma genie ai

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3d text-to-3d generation model luma genie research preview

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3d text-to-3d generation model luma genie research preview ai

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Genie, a research preview of Luma's generative 3d foundation model

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An exclusive research preview of Genie, a 3d generative foundation model

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Malaysia Gen AI Guidelines for Higher Education

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Malaysia Gen AI Guidelines for Higher Education

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UNESCO Guide for Generative AI in Education and Research

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UNESCO Guide for Generative AI in Education and Research

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Chat with PDF and conduct your literature review faster using SciSpace. Discover 200M+ papers or upload your own PDF, highlight text or ask questions, and extract explanations and summaries.

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"The best thing is that you don't have to go through all the struggle to figure out what words are required for your research and you can just sit back put a link of paper that you already have and start reading."

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"Having papers suggested that are both earlier and later than those already on my list, as well as the similar papers function, have helped me to find work I would have needed to pore over reference lists for ages to find otherwise. It's a huge time-saver!"

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"It's intuitive and tremendously useful. You save a lot of time identifying the most influential papers in a topic/field. Every scientist should know this!"

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- An Analytical Rabbit

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The most powerful discovery app ever built for researchers 🔥

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For builders of consumer applications, enabling scaled applications that grow into user needs and organically evolve through experience.

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A Toolkit on Generative AI Tools for Higher Education

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A Toolkit on Generative AI Tools for Higher Education By Associate Professor Ts Dr Helmi Norman, Universiti Kebangsaan Malaysia