The DOI matches its Crossref record; 6 of the 7 references it cites were found in a registry or an index; none of the 34 sources its claims cite is checked for support yet.
AI in education: Enhancing learning experiences and student outcomes
Open accessAbstract
This research article makes an attempt to investigate the potential of Artificial Intelligence (AI) in enhancing the learning experiences, as well as student outcomes. As a result, it has developed a study that will be able to understand how the different AI tools, including machine learning, data learning, virtual reality (VR) and augmented reality (AR), automation, and so forth can be used to develop learning experiences and outcomes. Subsequently, a case study involving a mathematics classroom was used to collect data and confirm whether indeed AI led to improved learning experiences and study outcomes. The study confirms that AI resulted in positive outcome with positive performance measure sin academic performance, motivation and engagement, learning progression, and so forth.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper argues that AI tools—personalized learning, intelligent tutoring, adaptive assessment, automated grading, data analytics, and VR/AR—can improve learning experiences and student outcomes. It supports this with a literature review and a case study of an AI-powered personalized learning platform in a ninth-grade algebra class of 30 students.
Key findings
- In the case study, post-assessment scores showed a higher average grade than pre-assessment scores, which the paper interprets as improved understanding and mastery of algebraic concepts.
- The AI platform reportedly increased student engagement and motivation, with students reporting feeling more connected to the material and more positive toward mathematics.
- Students advanced through the curriculum at different paces, received targeted support for difficult concepts, and showed improved self-efficacy and confidence in solving algebraic problems.
- The paper also reports that AI-driven automated grading provided prompt feedback, enhanced teacher-student interactions, and helped students apply algebraic knowledge to real-world scenarios.
Methods
- The paper combines a literature review on AI in education with a case study of AI-powered personalized learning in a public high school.
- The case study involved a ninth-grade algebra class of 30 students; the AI platform integrated adaptive assessments, intelligent tutoring, and personalized learning pathways.
- Data collected included pre- and post-assessment scores, time spent on the platform, engagement metrics, and student feedback through surveys and focus groups; the results are reported descriptively rather than through formal statistical analysis.
Limitations
- The paper does not state limitations.
- Evident from the text: the empirical evidence comes from a single ninth-grade algebra classroom of 30 students, with no control or comparison group.
- Evident from the text: results are reported qualitatively, without effect sizes, significance tests, or detailed numerical outcomes beyond the higher average post-assessment grade.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
24 Not checked yet · 9 Own interpretation · 6 Own result · 6 No basis given
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
“Students demonstrated the ability to apply their algebraic knowledge to real-world scenarios, showcasing the effectiveness of AI in promoting meaningful learning outcomes.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
boosted- 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.
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
“The AI-driven automated grading and feedback system provided prompt and constructive feedback to students.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
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
“The AI platform's ability to track individual learning progress allowed teachers to provide targeted support and personalized interventions.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
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 implementation of AI-powered personalized learning yielded significant improvements in learning experiences and student outcomes.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
- Rests on an assumption: The observed improvements in the case study are caused by the AI-powered personalized learning platform rather than other factors such as teacher effects, normal instruction, or novelty.
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
“Post-assessment scores indicated a higher average grade compared to preassessment scores”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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 analysis of learning progress showed that students advanced through the curriculum at different paces, reflecting the flexibility of AI-generated learning pathways.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
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
“Students reported feeling more capable and prepared to tackle challenging mathematical tasks.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
- Rests on an assumption: Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
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 study revealed that personalized learning experiences boosted students' self-efficacy and confidence in solving algebraic problems.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
boosteddirect- 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.
- Rests on an assumption: Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
In the paper
“A study by Vygotsky demonstrated that students exposed to personalized learning experiences showed higher levels of achievement and a deeper understanding of concepts compared to traditional classroom instruction [3].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.1. Personalized Learning
What the sources say
- Not checked yetcites [3]
boosteddirect- 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.
In the paper
“Thirdly, AI creates inclusive learning environments by accommodating different learning paces and abilities, ensuring that no student is left behind [4].”
§ AI in education: Enhancing learning experiences and student outcomes > Background of AI use in Education and its importance
What the sources say
- Not checked yetcites [4]
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. 21
- Probably not a substantial claim. 1
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
“Students who struggled with specific concepts received targeted support, leading to smoother learning progress and reduced frustration.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
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
“Students reported feeling more connected to the learning material, resulting in a more positive attitude towards mathematics.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
- Rests on an assumption: Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
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 AI platform's interactive nature and personalized content significantly increased student engagement and motivation.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
- Rests on an assumption: Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
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 case study of mathematics students also confirms this by observing that the use of AI led to improved academic performance, engagement and motivation, learning progression, timely and personalized feedback, among others.”
§ AI in education: Enhancing learning experiences and student outcomes > 10. Conclusion
plaindirectSingle case- 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: The observed improvements in the case study are caused by the AI-powered personalized learning platform rather than other factors such as teacher effects, normal instruction, or novelty.
None of the sources cited for this claim have been checked yet.
In the paper
“Researchers have found that tailoring learning materials to individual student needs leads to increased engagement, motivation, and knowledge retention.”
§ AI in education: Enhancing learning experiences and student outcomes > 2.1. Personalized Learning
What the sources say
- Not checked yetcites [3]
- Not checked yetcites [4]
- Not checked yetcites [5]
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Some studies have indicated that ITS, utilizing natural language processing, offered adaptive feedback and explanations, positively impacting student problem-solving skills and conceptual understanding [6,7].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.2. Intelligent Tutoring Systems (ITS)
What the sources say
- Not checked yetcites [6]
- Not checked yetcites [7]
hedgeddirect- 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, students in personalized learning environments exhibited greater self-efficacy and a more positive attitude toward learning [5].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.1. Personalized Learning
What the sources say
- Not checked yetcites [5]
plaincorrelational- 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.
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
“In summary, it is evident that AI has the capability to promote learning in schools, as well as improve outcomes.”
§ AI in education: Enhancing learning experiences and student outcomes > 10. Conclusion
plainconditional- 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: The 30 ninth-grade algebra students in one public high school are representative enough to generalize AI's impact to education broadly.
In the paper
“AIpowered adaptive assessments continually assess student progress and identify learning gaps.”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [8]
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
“Students can explore virtual worlds, conduct experiments, and interact with simulated environments, enhancing their understanding of complex concepts [18].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [18]
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 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
“By providing tailored content, real-time feedback, and personalized support, AI technologies contributed to improved academic performance, increased engagement, and a more positive learning environment.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
plaindirectSingle case- 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: The observed improvements in the case study are caused by the AI-powered personalized learning platform rather than other factors such as teacher effects, normal instruction, or novelty.
In the paper
“Students in adaptive assessment environments showed increased confidence and willingness to tackle challenging problems, positively influencing their learning experiences and outcomes [9].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [9]
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
“For instance, Wang and Sun demonstrates that VR and AR simulations enhanced students' conceptual understanding and critical thinking skills [13].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.6. Virtual Reality (VR) and Augmented Reality (AR) in Education
What the sources say
- Not checked yetcites [13]
boosteddirect- 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
“Researchers such as Robinson and Jackson highlighted the value of data analytics in identifying at-risk students, designing personalized interventions, and optimizing learning pathways [12].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.5. Educational Data Analytics
What the sources say
- Not checked yetcites [12]
plaindirect- 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.
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 timely feedback empowered students to make immediate improvements and reinforced their understanding of the subject matter.”
§ AI in education: Enhancing learning experiences and student outcomes > 9. Results and Discussion
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.
- Rests on an assumption: Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
In the paper
“As a result, students can master topics more effectively, leading to improved academic performance and higher levels of subject mastery [6].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [6]
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
“In fact, AR simulations exhibited deeper critical thinking skills and improved problem-solving abilities, underlining the impact of experiential learning through immersive technologies [18].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [18]
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
“This enables educators to design personalized interventions and provide additional support to students who may be struggling in specific areas [8].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [8]
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.
It may state two separate claims as one.
In the paper
“As a result, students receive tailored support and resources that align with their individual needs, promoting academic success and fostering a sense of belonging and support within the educational community [17].”
§ AI in education: Enhancing learning experiences and student outcomes > 4. Impact on Learning Experiences and Student Outcomes
What the sources say
- Not checked yetcites [17]
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.
None of the sources cited for this claim have been checked yet.
In the paper
“Some of the researchers indicated that automated grading saved educators time, enabling them to focus on personalized instruction and support [10,11].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.4. Automated Grading and Feedback
What the sources say
- Not checked yetcites [10]
- Not checked yetcites [11]
hedgeddirect- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Some of the Studies demonstrated that adaptive assessments tailored to student abilities resulted in improved performance and reduced test anxiety [7,8,9].”
§ AI in education: Enhancing learning experiences and student outcomes > 2.3. Adaptive Assessments
What the sources say
- Not checked yetcites [7]
- Not checked yetcites [8]
- Not checked yetcites [9]
boosteddirect- 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.
The observed improvements in the case study are caused by the AI-powered personalized learning platform rather than other factors such as teacher effects, normal instruction, or novelty.
“The implementation of AI-powered personalized learning yielded significant improvements in learning experiences and student outcomes.”
The paper relies on this without support.
The 30 ninth-grade algebra students in one public high school are representative enough to generalize AI's impact to education broadly.
“The case study demonstrated that AI-powered personalized learning positively impacted learning experiences and student outcomes in the high school mathematics classroom.”
The paper relies on this without support.
Student self-reports of engagement, motivation, self-efficacy, and confidence accurately reflect actual learning experiences and outcomes.
“Students reported feeling more connected to the learning material, resulting in a more positive attitude towards mathematics.”
The paper relies on this without support.
The AI platform was implemented and used as intended by all students throughout the academic year.
“An AI-powered personalized learning platform was introduced, which integrated adaptive assessments, intelligent tutoring, and personalized learning pathways.”
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?
In the citing paper
“This aligns with previous studies, such as those by Strielkowski et al. [11] and Xu [57], which emphasize the transformative potential of AI in improving academic outcomes through adaptive learning environments.”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [57]
Citations
Cites
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364
Adaptive intelligent tutoring systems for e-learning systems
Phobun, P., & Vicheanpanya, J. (2010) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2010.03.641 · Cited by 179 · cited 2×
“Some of the Studies demonstrated that adaptive assessments tailored to student abilities resulted in improved performance and reduced test anxiety [7,8,9].”
BackgroundA Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language
Paladines, J., & Ramírez, J. (2020) · IEEE Access · doi:10.1109/access.2020.3021383 · Cited by 143 · cited 1×
“Intelligent tutors use NLP to comprehend students' questions, offer explanations, and assess their understanding [16].”
BackgroundEducation, privacy, and big data algorithms: Taking the persons out of personalized learning
Regan, P. M., & Steeves, V. (2019) · First Monday · doi:10.5210/fm.v24i11.10094 · Cited by 30 · cited 2×
“These algorithms analyze vast amounts of student data, including performance history, learning preferences, and strengths [14].”
BackgroundDesign and Research of Intelligent Tutor System Based on Natural Language Processing
Wang, Y., Sun, Y. S., & Chen, Y. (2019) · IEEE International Conference on Computer Science and Educational Informatization (CSEI) · doi:10.1109/csei47661.2019.8939031 · Cited by 19 · cited 1×
“These systems can engage in natural language conversations with students, providing personalized guidance and support [8, 15].”
Background
Research works printed but not linked: 12
- Vygotsky, L. S., Johnson, M., & Smith, R. (2020). Personalized Learning: Enhancing Student Engagement and Academic Achievement. Journal of Educational Psychology, 45(3), 321-337.
- Lee, H., & Wu, T. (2019). Adaptive Assessments: Identifying Learning Gaps and Improving Performance in Mathematics. Journal of Educational Assessment, 18(1), 89-104.
- Johnson, A., & Smith, B. (2019). The Impact of Personalized Learning on Student Attitudes and Self-Efficacy in Mathematics. Educational Technology Research and Development, 38(2), 201-218.
- Rolland, C., Thompson, P., & Williams, J. (2018). Intelligent Tutoring Systems: Enhancing Problem-Solving Skills and Conceptual Understanding in Physics Education. Computers & Education, 25(4), 567-580.
- Lee, H., & Wu, T. (2019). Adaptive Assessments: Identifying Learning Gaps and Improving Performance in Mathematics. Journal of Educational Assessment, 18(1), 89-104.
- Chen, Y., Zhang, X., & Wang, L. (2020). The Impact of Adaptive Assessments on Test Anxiety and Academic Achievement. Educational Psychology Review, 35(2), 211-226.
- Smith, E., & Brown, K. (2018). Automated Grading and Feedback: Enhancing Efficiency and Student Satisfaction. Journal of Educational Technology Integration, 42(1), 67-82.
- Li, M., Johnson, P., & Williams, S. (2021). Automated Grading and Student Perceptions: A Comparative Study. Journal of Educational Assessment, 29(3), 301-315.
- Robinson, D., & Jackson, L. (2019). Educational Data Analytics: Empowering Evidence-Based Decision-Making in Higher Education. International Journal of Educational Management, 36(4), 567-584.
- Wang, C., & Sun, Y. (2018). The Immersive Potential of Virtual Reality and Augmented Reality in Education. Educational Technology & Society, 22(3), 387-401.
- Wang, J., Zhang, Q., & Liu, W. (2020). Leveraging Data Analytics for Personalized Interventions in Online Learning Environments. Computers & Education, 30(1), 89-104.
- Kim, H., Lee, S., & Park, J. (2021). Augmented Reality Simulations: Enhancing Critical Thinking Skills in Science Education. Journal of Educational Technology Research, 38(4), 556-570.
Cited by
Sorted by citations
Systematic review of artificial intelligence in education: trends, benefits, and challenges
Garzón, J., Patiño, E., & Marulanda, C. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9080084 · Cited by 265
“This aligns with previous studies, such as those by Strielkowski et al. [11] and Xu [57], which emphasize the transformative potential of AI in improving academic outcomes through adaptive learning environments.”
Advancements in artificial intelligence transforming medical education: a comprehensive overview
Khakpaki, A. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2542807 · Cited by 109
“However, challenges such as high implementation costs and the need for technical training must also be addressed to ensure widespread adoption [72,75].”
Egara, F. O., & Mosimege, M. D. (2024) · Education Sciences · doi:10.3390/educsci14070742 · Cited by 124
“Additionally, similar studies by Jian [66] and Xu [67] have shown that AI tools can enhance classroom engagement and facilitate personalised learning when effectively integrated.”
Jeilani, A., & Abubakar, S. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1548900 · Cited by 64
Guizani, S., Mazhar, T., Shahzad, T., Ahmad, W., Bibi, A., & Hamam, H. (2025) · Discover Education · doi:10.1007/s44217-025-00424-7 · Cited by 99
“AI algorithms can analyse a student’s past performance on assignments and tests, allowing for the provision of personalized recommendations to assist the student in realizing their maximum potential [88].”
Study on the Impact of Artificial Intelligence on Student Learning Outcomes
Sasikala, P., & Ravichandran, R. (2024) · Journal of Digital Learning and Education · doi:10.52562/jdle.v4i2.1234 · Cited by 76
“AI technologies, including machine learning, virtual reality, and intelligent tutoring systems, have been shown to enhance academic performance, motivation, and engagement (Xu, 2024; Suntharalingam, 2024).”
BackgroundKayyali, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2373-0.ch007 · Cited by 42
Sustainable Innovation: Harnessing AI and Living Intelligence to Transform Higher Education
Allam, H., Gyamfi, B. K., & AlOmar, B. (2025) · Education Sciences · doi:10.3390/educsci15040398 · Cited by 60
“AI-powered systems can provide continuous, personalized student support by predicting individual learning needs and proactively addressing academic challenges (Nimbalagundi et al., 2024; Xu, 2024).”
Younas, M. S., El‐Dakhs, D. A. S., & Noor, U. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1689205 · Cited by 58
Ahmad, W., Shokeen, R., & Raj, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5538-1.ch017 · Cited by 22
Kayaalp, F., Durnalı, M., & GÖKBULUT, B. (2024) · European Journal of Education · doi:10.1111/ejed.12865 · Cited by 30
AI'S Impact on Vocational Training and Employability: Innovation, Challenges, and Perspectives
-, R. E. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i04.24967 · Cited by 30
“Virtual reality may provide immersive, hands-on training that simulates real-world events, improving practical skills and preparation for genuine job contexts [34].”
BackgroundMalik, I. A., Adem, S. A., & Rasool, A. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2546126 · Cited by 22
“Academicians and researchers acknowledge the potential of EdAI to enhance learning outcomes by providing personalized instructions and data-driven learning insights (Xu, 2024).”
Isaifan, R. J., & Hasna, M. O. (2025) · Quality in Higher Education · doi:10.1080/13538322.2025.2576326 · Cited by 12
“…reported opportunities and applications of using AI in higher education institutions as shown in Table 1, AI-driven systems can analyse huge datasets of student performance, providing educators with real-time insights to tailor instructional strategies and enhance student engagement (Xu, 2024).”
Generative AI-Based Tutoring for Enhancing Learning Engagement and Achievement
Belawati, T., & Prasetyo, D. (2025) · Open Praxis · doi:10.55982/openpraxis.17.2.902 · Cited by 19
Enhancing Education and Well-Being Through Artificial Intelligence: Opportunities and Challenges
Qasmi, F., & Fatima, S. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-65996-6_13 · Cited by 12
Avsec, S., & Rupnik, D. (2025) · Systems · doi:10.3390/systems13070562 · Cited by 18
Wen, Q., Wang, J., Guo, Z., & Bădulescu, D. (2025) · Behavioral Sciences · doi:10.3390/bs15121649 · Cited by 11
“…Najmiddinova, 2024), etc. Previous studies have also shed light on how Gen-AI in education and academia enhances social development among students and teachers by fostering collaboration, communication skills, and self-esteem through adaptive, game-like activities (Grace et al., 2023; Z. Xu, 2024).”
Mission, R. S., Fio, R. J., & Mission, A. R. (2024) · Journal of Innovative Technology Convergence · doi:10.69478/jitc2024v6n4a06 · Cited by 15
Personalized Learning and Student Engagement
Babu, C. V. S., Yuvansankar, M., & Tharuneshwaran, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7195-4.ch004 · Cited by 9
Alsswey, A. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105190 · Cited by 10
AI literacy as a key driver of user experience in AI-powered assessment: insights from Socratic mind
Soylu, M. Y., Lee, J., Hung, J.-T., Cui, C., & Joyner, D. A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2564739 · Cited by 23
“Research suggests that students with higher AI literacy may engage more productively, calibrate trust in AI feedback, and navigate AI-mediated tasks with greater confidence, whereas those with lower literacy may struggle to realize the same benefits (Walter, 2024; Xu, 2024).”
Integrating Artificial Intelligence in Media Education
Jha, A., & Singh, S. R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3373-1017-6.ch010 · Cited by 9
Mariñas, K. A., Saflor, C. S., Alvarado, P., Uminga, J. M., & Verde, N. A. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100435 · Cited by 18
Artificial intelligence integration and teachers’ self-efficacy in physics classrooms
Yehya, F. M., ElSayary, A., Murshidi, G. H. A., & Zaabi, A. A. (2025) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/16660 · Cited by 9
Tariq, M. U. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4576-5.ch002 · Cited by 8
Artificial Intelligence for Virtual Reality: State of the Art, Challenges, and Future Perspectives
Wang, Y., Guizani, M., & Hossain, M. S. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3769090 · Cited by 10
Amer, F. G. M., Alanazi, M. A., Shaban, M., Abdelgawad, M. E., Mohammed, H. H., Hassan, S., Atrous, M. H., Ibrahim, S. S. M., Mohamed, A. H., & Sabek, E. M. (2025) · International Nursing Review · doi:10.1111/inr.70056 · Cited by 12
Alqarni, O. M., Curle, S. M., Mahdi, H. S., & Ali, J. K. M. (2024) · Forum for Linguistic Studies · doi:10.30564/fls.v6i5.6975 · Cited by 12
“It supports personalized learning paths, adaptive assessments, and real-time feedback, which contribute to heightened student engagement and academic performance [30, 31] .”
Lamrabet, M. (2025) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2025.15.4.2277 · Cited by 11
“Specifically, these analyses demonstrated that both future and experienced teachers with satisfactory knowledge of AI-based tools tend to perceive these tools positively and are motivated to integrate them into their teaching practices from the initial stages of their careers [71–73].”
AI-Enabled Learning Ecosystems
Hari, S. S., Rajashree, R. H., Dharani, J., & Santhiya, C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8292-9.ch008 · Cited by 6
Tressyalina, T., Ghaluh, B. M., Wulandari, E., Arief, E., & Noveria, E. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2504062 · Cited by 5
“Such discussions can be enriched by analyzing AI-generated interpretations of texts and videos, encouraging students to critically evaluate technological outputs and their societal implications (Xu, 2024).”
The Role of AI-Powered Learning Analytics in Enhancing Student Engagement and Performance
Tariq, M. U. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2185-1.ch005 · Cited by 5
Khojasteh, L., Karimian, Z., Nasiri, E., Kafipour, R., & Farahmandi, A. Y. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08288-z · Cited by 10
“AI tools, while enhancing writing quality and efficiency, can blur the lines between students’ original work and AI-assisted content, raising questions about the authenticity of academic writing [9].”
Hwang, W., Nguyen, T.-R.-G., & Shadiev, R. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70208 · Cited by 7
Harnessing Artificial Intelligence in Education: Innovations, Opportunities, and Challenges
Cardona, M., Cáceres, C., Cáceres, A., & Aldana-Aguilar, J. (2024) · IEEE Central America and Panama Convention (CONCAPAN) · doi:10.1109/concapan63470.2024.10933829 · Cited by 4
“Intelligent tutoring systems, such as Knewton, adapt the material and teaching pace according to individual needs [14].”
Faculty AI literacy in higher education systems: a systematic review and future-adaptive framework
Wu, Y., & Huang, Y. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2648739 · Cited by 7
AI-Driven Transformation of Higher Education a Systematic Review
Erlangga, A. P. D., & Magdalena, Y. (2025) · International Conference on Creative Communication and Innovative Technology (ICCIT) · doi:10.1109/iccit65724.2025.11167254 · Cited by 8
“The increasing of AI implementation enables higher education institutions to find creative and innovative approaches to improve students engagement, personalize learning experiences, and streamline operations [2].”
Basic sciences are non-negotiable: debunking three critical fallacies in engineering education
Guerra, J., & Meneses-La-Riva, M. E. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1600009 · Cited by 5
Zhang, X. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1595818 · Cited by 4
“AI-driven learning platforms can enhance learner immersion through continuous interaction with students (Xu, 2024).”
Rahman, M. H., Islam, M. M., Islam, M. N., & Nasiruzzaman, M. (2026) · The Electronic Library · doi:10.1108/el-04-2025-0159 · Cited by 3
Park, E. K. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2619983 · Cited by 7
“Their intention to engage with tools can be affected by the comprehensive and complex interplay of cognitive, psychological, environmental, and technological communication (Z. Xu 2024).”
Artificial intelligence as a reflexive collaborator in graduate studies supervision
Brown, A., & Rossouw, J. (2026) · Transformation in Higher Education · doi:10.4102/the.v11i0.657 · Cited by 6
Asanre, A. A., Oguntola, J. I., Ramatea, M. A., & Lawani, A. O. (2026) · European Journal of STEM Education · doi:10.20897/ejsteme/18268 · Cited by 4
“Similarly, Yeh et al. (2019), Szabo et al. (2020), Xu (2024), and Zaman (2023) documented that AI-supported learning environments strengthen both immediate understanding and long-term retention.”
Ullah, R. S., Asghar, A., Ali, M. I., & Jan, R. (2024) · Review of Education Administration and Law · doi:10.47067/real.v7i4.393 · Cited by 3
Transforming Personalized Learning With Artificial Intelligence
Revathy, J. S., M., R., S., A. M., & G., M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8292-9.ch014 · Cited by 2
Incorporating AI in Educational Leadership
Ruheili, H. A., & Al-Buraiki, S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7863-2.ch009 · Cited by 2
Empowering Students With Disabilities in Education Through AI-Driven Approaches
Bhimavarapu, U. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1000-8.ch006 · Cited by 2
Pesovski, I., Jolakoski, P., Trajkovik, V., Kubincová, Z., & Herzog, M. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100430 · Cited by 2
Moore, S., Jackson, L. E., Kwon, C., & Stamper, J. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03870-8_25 · Cited by 2
Mansoor, H. S., Sumardjoko, B., & Sutopo, A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1719955 · Cited by 8
“Integration of AI in education, increases student engagement through customized learning, improves learning outcomes, and saves time (Xu, 2024).”
Integrating AI Education Strategies for Future-Proofing Curriculum Design in Omani Universities
Awashreh, R. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2752-5.ch008 · Cited by 2
Designing Inclusive Education With AI-Driven Pedagogies and Learning Analytics
Alam, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5188-9.ch004 · Cited by 3
MRAYYAN, M. T., & Alomari, A. K. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/9885653 · Cited by 3
Artificial intelligence enabled smart education systems
Sharma, A. K., & Kumar, T. V. V. (2026) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-026-03196-x · Cited by 3
Pum, M., Heng, K., & Sok, S. (2026) · Open Praxis · doi:10.55982/openpraxis.18.3.1047 · Cited by 2
Sri, S. S., Rajitha, K., Muraleedharan, D., Premkishor, S. K., Elakki, R. T., & Prabhakaran, S. (2025) · Journal of Ecohumanism · doi:10.62754/joe.v4i1.6042 · Cited by 3
“Additionally, concerns around data privacy and the ethical use of AI in student assessments have been raised, with critics arguing that overreliance on AI may depersonalize education (Xu, Z., 2024).”
Designing an AI Expert of Educational Programmes in Higher Education
Davlatova, M., Shalom, K., & Kobtseva, A. (2025) · doi:10.1109/icaie64856.2025.11158591 · Cited by 2
“The active implementation of AI technologies leads to new opportunities for the automation of expertise, the reduction of time and financial costs, and the enhancement of objectivity in assessments [12].”
Ndjama, J. D. N., & Westhuizen, J. V. D. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9989-7.ch005 · Cited by 1
Students' Learning Math With the Aid of AI and the Need of Shifting Classroom Instructions
Jackaria, P. M., Sali, A.-A. G., Alvarado, H. A. L., Jiripa, R. H. M., & Sahijuan, A.-S. Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0847-0.ch002 · Cited by 1
Teaching the Past With the Future
Şahin, A. N. E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2612-2.ch008 · Cited by 1
Classroom Engagement through Real-time Student Attention Analysis using YOLO V8
Goh, M. L. I., Juanatas, I. C., Juanatas, R. A., et al. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075414 · Cited by 1
“One study evaluated AI-based systems in classrooms and demonstrated that real-time monitoring helps educators assess student engagement levels and adapt their teaching strategies accordingly [4].”
Liu, F., Chen, F., Teng, Y., Dai, Q., Wang, Q., Shen, J., & Zhao, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0017-8_35 · Cited by 1
Das, P., Harshan, Harsha, C., Suman, H. K., & Giri, A. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075412 · Cited by 1
“The potential benefits of Generative AI in education are further demonstrated by Xu, Zhiyi (2024) [11], which discusses how AI can enhance learning experiences and student outcomes by enabling personalized skill development and upskilling.”
Redefining the Role of Educators, Not Replacing Them
Terantino, J., Zaheer, S., & Zhu, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0396-3.ch009 · Cited by 1
Nguyen, Q. N., & Hue, D. D. T. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1636777 · Cited by 11
“One recent review by Xu (2024) highlights numerous instances where AI-driven systems improved learning outcomes.”
MacLennan, H., Malka, S. C., & Queiroz, H. J. D. S. D. (2025) · SAM Advanced Management Journal · doi:10.1108/samamj-04-2025-0023 · Cited by 2
“How about our stance?”: college students’ stance on using artificial intelligence educational tools
Bringula, R. P., Trillanes, A. O., & Catacutan-Bangit, A. E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13859-0 · Cited by 2
Al-sendy, A. M. A., Jameel, S. Z. M., Al-Shmam, M. A., Riyadh, H. A., & Beshr, B. A. H. (2025) · International Journal of Educational Management · doi:10.1108/ijem-12-2024-0820 · Cited by 4
Altan, B. A., & Kabaran, G. G. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13871-4 · Cited by 3
Adoption of Gemini AI to Enhance Student Learning
Prakash, V., Ebinraj, A. C., & Appukuttan, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7526-5_43 · Cited by 1
Ma, R., & Guo, M. (2026) · Behavioral Sciences · doi:10.3390/bs16040577 · Cited by 2
“Similarly, Xu’s study shows that in AI-enhanced learning environments, students with higher AI literacy can navigate complex AI tools, interpret algorithmic outputs, and personalize learning experiences (Xu, 2024).”
A Study on EduTainment: Enhancing Learning Activity in Science Learning
Susilowaty, A. K., Nugraha, N., & Hartono, Y. (2026) · Journal Evaluation in Education (JEE) · doi:10.37251/jee.v7i2.2656 · Cited by 1
Pham, T., & Ngo, T. T. A. (2026) · Telematics and Informatics Reports · doi:10.1016/j.teler.2026.100344 · Cited by 2
Polinar, M. A. N., Mahipos, A. M. N., Rabanes, B. G., & Ato, J. M. U. (2025) · Diversitas Journal · doi:10.48017/dj.v10i2.3345 · Cited by 3
Ren, Y., Ren, J., Chen, Y., & Liu, Q. (2025) · Frontiers of Digital Education · doi:10.1007/s44366-025-0065-8 · Cited by 3
“AI tailors learning paths to individual student needs, provides real-time feedback (Xu, 2024), optimizes teaching content, and enables objective assessment.”
AI Educational Support Framework for At-Risk Media Students
Assad, A. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075419 · Cited by 1
“In addition, the AI tools reduce the gap between at-risk and other students and enhance their motivation and learning satisfaction [7].”
Pedro, J. R. S., & Ting, F. S. T. (2026) · Research Square · doi:10.21203/rs.3.rs-8835102/v1 · Cited by 1
AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-EsteemPreprint
Chakraborty, D., & Subramani, D. (2026) · Research Square · doi:10.21203/rs.3.rs-9043305/v1 · Cited by 1
Doğum Şekli ve Doğum Korkusuna Etki Eden Faktörler
Güven, N., & Doğan, R. A. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub528.c2147
Integrating Text Analysis and LLM to Explore the Development and Application of AIGC in Education
Wang, S.-M., & Wu, Y. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10826827
Malka, S. C., MacLennan, H., & Queiroz, H. D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5108051
Inclusive Curriculum Development and Implementation in Zimbabwe
Chidindi, J. (2025) · Springer eBooks · doi:10.1007/978-3-031-66870-8_5
Kim, D. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251321544
“Arti fi cial intelligence is automating routine tasks while amplifying the demand for higher-order thinking, adaptability, and lifelong learning (Xu, 2024).”
Virtual Tutor: AI-Powered Learning Platform
Kushwaha, R. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.70698
Hegazy, A. Z. (2025) · International Journal of Innovative Research and Scientific Studies · doi:10.53894/ijirss.v8i3.7049
“Similarly, it was underlined by Jian [42] and Xu [43] that AI tools like ChatGPT would improve classroom interaction, with productivity and overall learning reaching their maximum when efficiently inculcated.”
InfluentialHasheela, V., Haponnen, A., & Tuape, M. (2025) · doi:10.36227/techrxiv.174802366.64034276/v1
Design and Evaluation of an Adaptive VLE for a Flipped Classroom Emergency Care course
Gouifrane, R., Lajane, H., Arai, M., et al. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0740-4.ch010
Malka, S. C., MacLennan, H., & Queiroz, H. J. D. S. D. (2025) · Journal of Higher Education Theory and Practice · doi:10.33423/jhetp.v25i2.7669 · Cited by 1
Al-Modafar, F. A. (2025) · International Journal of Education and Practice · doi:10.18488/61.v13i3.4268
Alhazzani, N. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-22205-3_21 · Cited by 1
Dey, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5102-5.ch003
Enhancing Active Learning in Education Through ChatGPT and Concept Maps: A Case Study
Schicchi, D., & Taibi, D. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94002-6_1 · Cited by 1
Angwaomaodoko, E. A. (2025) · Path of Science · doi:10.22178/pos.120-27
Antony, M. K., Pradana, S. P., Ramadhan, A. N., Dewiyeti, S., & Hidayati, E. (2025) · Jurnal Penelitian Pendidikan IPA · doi:10.29303/jppipa.v11i7.11004 · Cited by 5
“Through this approach, students are encouraged to take an active role in inquiry-based learning, which enhances both their conceptual understanding and higher-order thinking skills (Harry, 2023; Favero, 2024; Xu, 2024).”
Cognitive AI’s Transformative Impact on Self-Regulated Learning in eLearning Environments
Hadyaoui, A., Cheniti‐Belcadhi, L., & Mitwally, M. A. A. (2025) · Future education and learning spaces · doi:10.1007/978-981-96-7937-9_2 · Cited by 1
AI-Based Pattern Recognition Model for Enhancing Student Engagement and Supporting Academic PlanningPreprint
Bao, H. G. (2025) · Research Square · doi:10.21203/rs.3.rs-7644175/v1
AI-Based Pattern Recognition Model for Enhancing Student Engagement and Supporting Academic Planning
Bao, H. G., & Đại, N. V. (2025) · Seminars in Medical Writing and Education · doi:10.56294/mw2025846 · Cited by 1
Harnessing Project-Based and Experiential Learning for Competency-Driven Education
Ramaila, S. M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3566-7.ch004
Enhancing Learning Experiences Using Artificial Intelligence Technology in Education
Rajagopal, S., Deevi, D., Ranjna, et al. (2025) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams65118.2025.11234168
“Artificial intelligence (AI) has lately changed the regulations in several areas, including teaching, by altering demand and learning centre plans [1–5].”
Metadata
- Authors
- Xu, Z.
- Affiliations
- Jilin University (CN)
- Corresponding author
- Xu, Zhiyi
- Venue
- Applied and Computational Engineering
- Publisher
- EWA Publishing
- Published
- 2024-03-25
- Volume
- 51
- Issue
- 1
- Pages
- 104-111
- ISSN
- 2755-2721 (ISSN-L)
- 2755-273X
- OpenAlex
- W4393089565
- Semantic Scholar
- 11c88602d4f73937d2fe6456088dee04dd189914
- Cited by
- 151
- References
- 7
- Citations by year
- 2026: 57 · 2025: 80 · 2024: 14
- FWCI
- 56.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Applied and Computational Engineering (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Educational Technology and Pedagogy
- Keywords
- artificial intelligence in education, learning experience, student outcomes, machine learning, virtual reality, augmented reality, educational automation, mathematics education, academic performance, student motivation, student engagement, learning progression
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