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
Items 101–145 of 145
245Chapter 13 AI and data analytics in AR and VR
Hanees, A. L., Natarajan, G., Nagasubramanian, G., et al. (2025) · De Gruyter eBooks · doi:10.1515/9783111388588-013
The Transformative Impact of AI on Student Development and Learning
Ramaila, S. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2927-7.ch011
Do Social Robots Motivate Students like Humans?
Pande, A., Gosala, B., Gupta, M., & Mishra, D. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2379-5_8
Dil Öğreniminde Yapay Zekâ Sohbet Robotları: ChatGPT Örneği
DEMİREL, A. (2025) · International Journal of Languages Education · doi:10.71084/ijlet.1734989
Aydemir, M., & Başman, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-4871-2_10 · Cited by 2
Generation Alpha – the new career generation in the age of AI
Romek, K., & Kowalczyk-Kroenke, A. (2025) · Nowoczesne Systemy Zarządzania · doi:10.37055/nsz/217074
Angelo, J., Chu, M. M., Suhartono, J., Cassandra, C., & Halim, E. (2025) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact67584.2025.11344855
“The usage of AI has significantly improved learning by developing personalized learning experiences, offering students individualized support and feedback that boosts student engagement, motivation, and academic performance [15][19].”
AI and ML for Continuous Monitoring of Cognitive Learning
Kanthavel, R., Freeda, R. A., & Dhaya, R. (2026) · Springer eBooks · doi:10.1007/978-981-95-4925-2_5
Miranda, J. P. P., Manalese, R. P., Geronimo, S., Maniago, V. G. M., Padilla, C. K., Leon, A. P. D., Merle, S., & Castro, M. A. (2025) · International Workshop on Artificial Intelligence and Education (WAIE) · doi:10.1109/waie67422.2025.11381174 · Cited by 2
“Other research suggests that supervised and reflective use of AI, where learners edit or adapt the output, may strengthen learning outcomes [39], [44], [45].”
Intelligent Student Activity and Performance Monitoring System for Online Education
Nama, S., Raj, M., Singh, O. P., et al. (2025) · IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) · doi:10.1109/isssc66652.2025.11388775
“Furthermore, AI categorization logic offers a straightforward yet effective means of classifying student engagement, providing a foundation for advanced analytics in the future [14].”
Training K–12 Teachers for AI Integration
Suárez, L. M. C., GUTIÉRREZ-JIMÉNEZ, B., & Domínguez, N. P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0101-1.ch013
AI-Enhanced Learning Environments
Alkatheeri, S., & Mahmoud, E. Y. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0388-6.ch004
Ibrahim, K., Osuizugbo, I. C., Dandajeh, M. A., Omer, M. M., Oshodi, O. S., & Ezeudu, B. O. (2026) · Journal of structural design and construction practice. · doi:10.1061/jsdccc.sceng-1969 · Cited by 1
Pedro, J. R. S., & Buyco, H. (2026) · Research Square · doi:10.21203/rs.3.rs-9037619/v1
Blended Ensemble Intelligence for Academic Performance Prediction in Educational Datasets
Sharma, M., & Mansotra, V. (2026) · SN Computer Science · doi:10.1007/s42979-026-04919-4
Schwarte, Y., & Gao, Y. (2026) · Journal of International Education in Business · doi:10.1108/jieb-08-2024-0098 · Cited by 1
Rufin, S. M. M., Hanafi, H. F., Noor, W. H. M., & Yusof, S. S. M. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829051
EFFECT OF SOCIAL MEDIA ON MALAYSIAN EDUCATION: A MIXED-METHOD STUDY
Khan, A., Barkhaya, N. M. M., Sanguman, M., & Akhtar, M. N. (2026) · International Journal of Education Psychology and Counseling · doi:10.35631/ijepc.1162028
An Impresiv AI–Driven Academic Data Assistant for Intelligent Academic Information Systems
Nababan, A. A., Dewananda, K. F., Zarqan, I. A., Pangaribuan, J. J., Irwansyah, Priyatna, R. D., Figna, H. P., & Jannah, M. (2026) · International Symposium on Computational and Business Intelligence (ISCBI) · doi:10.1109/iscbi69404.2026.11496267 · Cited by 1
“These approaches limit flexibility and delay access to actionable insights for students, lecturers, and administrators [21], [22].”
Real-Time Classroom Engagement and Interaction Monitoring Using YOLOv5
Gokuldhev, M., Muhammed Sinan, P., & Dwivedi, K. P. (2026) · doi:10.1109/icsft66733.2026.11506822
“The real-time participation trends were provided in the Streamlit dashboard, enabling adaptive teaching and data-driven insights [4].”
Azmi, M., Hamzah, M. N., & Huzaen, C. M. Z. (2026) · Indonesian Journal of Multidisciplinary on Social and Technology · doi:10.69693/ijmst.v4i2.9033
Razzaq, S., McCarthy, P. M., & El-Soussi, A. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03464-7
“This finding is consistent with earlier research on feedback, which suggests that quality feedback directly enhances learners’ motivation and performance [6, 28, 93].”
Falvo, I., Benecchi, E., & Porlezza, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6866866
Addressing Challenges and Ethical Considerations in AI-Driven Faculty Development
Dey, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1285-9.ch002
Wu, J., Choi, D., Kim, B., & Hong, H. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812968
Enhancing Ethical Generative AI Literacy in Early Learners Through Educational Intervention
Tariq, R., & Masood, A. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574498
Revolutionizing Exam Preparation with AI: A Smart Platform for Adaptive and Personalized Learning
Senthilkumaran, B., Anand, A. S. A., Chowdari, A., & Pavan, C. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-7238-0_37
AI for Education: Bridging the Pandemic Learning Gap in the Aspirational Districts of Madhya Pradesh
Kumar, D., Kumar, S., & Tandon, S. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-981-96-9646-8_25
Dyosini, T. P., & Letsapa, M. (2026) · Journal of Education · doi:10.17159/2520-9868/i104a09
“Furthermore, Xu (2024) added that AI tools offer real-time feedback and personalised support, enhancing conceptual understanding while enabling differentiated instruction tailored to individual student needs.”
Forecasting EdTech: A Patent-Based Trajectory Analysis of AI Innovation in Education
Bhatt, P. C., & Kaur, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32115-2_34
Efficiency and Effectiveness of AI Applications in Human Resource Management
Stone, D. L., Lukaszewski, K. M., & Murray, B. (2026) · Oxford University Press eBooks · doi:10.1093/9780197851388.003.0580
AI-Supported Cross-Training Bridging Skill Gaps and Enhancing Workforce Readiness
Soomro, S., Soomro, S., & Raza, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5327-2.ch008
Sosyal Bilgiler Öğretmen Adaylarının Yapay Zekâ Destekli Materyal Kullanımına İlişkin Görüşleri
Bulut, B., & Demi̇r, G. T. (2026) · Dumlupınar Üniversitesi Eğitim Bilimleri Enstitüsü Dergisi · doi:10.71272/debder.1816077
Rafik, L., Alsaadani, S., & Abdel-Moneim, N. M. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-032-25471-9_10
Gabinete, S. G. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2027121
The use of AI-powered math platforms in mathematics learning
Abrigana, G. I., Caruana, R. M., Obcianal, J. R. L., Templa, E. M. S., Aleguen, J. M., & Elladora, S. T. (2026) · LUMAT International Journal on Math Science and Technology Education · doi:10.31129/lumat.14.1.2918
“Consistent with this, Xu (2024) noted that AI-PMPs improve students’ comprehension, academic performance, computational thinking, and problem-solving abilities.”
A Systematic Review of the Areas of Classroom Management and Curriculum Enhancement.
Sohail, T. B., Nazir, F., Sabahat, N., Zaib, M., & Qamar, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch003
AI-Augmented Creativity in Personalized Learning Pathways
Eziokwu, U. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch007
Artificial Intelligence in Changing Classroom Management and Teaching Support
Qamar, N., Sohail, T. B., Zaib, M., Nazir, F., & Salleh, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3663-1.ch002
Hung, H.-C., & Pawitra, M. A. S. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261483883
Olivera, P. C., Grájeda, A., & Verduguez, V. (2026) · Education Sciences · doi:10.3390/educsci16091430
Designing AI-Driven Assessments for Competency-Based Education in Higher Education
Dewi, M. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9089-5.ch002
Arithmetic with AI: enhancing learning or replacing thinking
Dhanapal, K., & Sivaprakkash, S. (2026) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-06-2025-0153
Vanhée, L., Boström, E., Lithner, J., Andersson, C., Palmberg, B., Winberg, M., Svahn, O., & Palm, T. (2026) · IEEE Access · doi:10.1109/access.2026.3733324
“A rapidly growing literature [101] demonstrates the prospects of AI systems for serving various educational stakeholders [7], [113], [139], including learners (e.g., by improving motivation, enjoyment, engagement, collaboration, autonomy, creativity, knowledge retention, higher-order thinking,…”
Aryani, L. N., Widiana, I. W., Suharta, I. G. P., Lasmawan, I. W., & Arnyana, I. B. P. (2026) · Discover Education · doi:10.1007/s44217-026-02177-3
“Failure to protect student data may expose institutions to serious ethical and legal consequences, particularly when AI is used as part of the curriculum evaluation process [63].”
Influential
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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