Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

Kochmar, Ekaterina; Vu, Dung Do; Belfer, Robert; Gupta, Varun; Serban, Iulian Vlad; Pineau, Joelle

doi:10.1007/978-3-030-52240-7_26

Cited by 119Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in EducationOnline Learning and Analytics

Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

Open access

Kochmar, Ekaterina, Vu, Dung Do, Belfer, Robert, Gupta, Varun, Serban, Iulian Vlad, Pineau, Joelle·2020·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-52240-7_26PMC PMC7334734

Open this paper in CoChat — free account

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/978-3-030-52240-7_26

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

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?

Intelligent Tutoring Systems offering personalised automated hints and explanations have been shown to improve learning outcomes and receive positive evaluation from students.
“Meanwhile, ITS's offering personalised automated hints and explanations have been shown to improve learning outcomes and receive positive evaluation from students [76, 130].”
From AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities (2026) · cites this paper as [76]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

AutoTutor: an intelligent tutoring system with mixed-initiative dialogue.
Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005) · IEEE Transactions on Education · doi:10.1109/te.2005.856149 · Cited by 750 · see citing works
Intelligent Tutoring Systems
Anderson, J. R., Boyle, C., & Reiser, B. (1985) · Science · doi:10.1126/science.228.4698.456 · Cited by 699 · see citing works
Untitled work
(2001) · doi:10.1609/aimag.v22i4.1591 · Cited by 381 · see citing works
Designing Learning by Teaching Agents: The Betty's Brain System
Leelawong, K., & Biswas, G. (2008) · International Journal of Artificial Intelligence in Education · doi:10.3233/irg-2008-18(3)02 · Cited by 311 · see citing works
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring
Nye, B. D., Graesser, A. C., & Hu, X. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0029-5 · Cited by 298 · see citing works
Data mining for providing a personalized learning path in creativity: An application of decision trees
Lin, C. F., Yeh, Y., Hung, Y. H., & Chang, R.-I. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.05.009 · Cited by 250 · see citing works
Peekaboo as an Instructional Model: Discourse Development at Home and at School
Cazden, C. B. (1983) · Springer eBooks · doi:10.1007/978-1-4899-1525-2_3 · Cited by 141 · see citing works
ActiveMath: An Intelligent Tutoring System for Mathematics
Melis⋆, E., & Siekmann, J. H. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24844-6_12 · Cited by 110 · see citing works
Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions
Chi, M., Koedinger, K. R., Gordon, G. J., Jordon, P., & VanLahn, K. (2018) · PubMed · doi:10.1184/r1/6475808.v1 · Cited by 68 · see citing works
Assessment with computer agents that engage in conversational dialogues and trialogues with learners
Graesser, A. C., Cai, Z., Morgan, B., & Wang, L. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.03.041 · Cited by 63 · see citing works
Adaptive Intelligent Tutoring System for learning Computer Theory
Al-Nakhal, M. A., & Abu-Naser, S. S. (2017) · PhilPapers (PhilPapers Foundation) · Cited by 46 · see citing works
Interactivity of Exercises in ActiveMath
Giorgi Goguadze, A., Palomo, A. G., & Erica Melis, C. (2005) · International Conference on Computers in Education · Cited by 34 · see citing works
An Intelligent Tutoring System for Learning Classical Cryptography Algorithms (CCAITS)
AbuEl-Reesh, J. Y., & Abu-Naser, S. S. (2018) · PhilPapers (PhilPapers Foundation) · Cited by 27 · see citing works
The Impact of Student Model Updates on Contingent Scaffolding in a Natural-Language Tutoring System
Albacete, P., Jordan, P., Katz, S., Chounta, I., & McLaren, B. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23204-7_4 · Cited by 26 · see citing works
Excel Intelligent Tutoring System
Qwaider, S. R., & Abu-Naser, S. S. (2018) · PhilPapers (PhilPapers Foundation) · Cited by 22 · see citing works
DeepTutor
Rus, V., Stefanescu, D. C., Niraula, N. B., & Graesser, A. C. (2014) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2556325.2567885 · Cited by 21 · see citing works
Preliminary Evaluations of a Dialogue-Based Digital Tutor
Ventura, M., Chang, M., Foltz, P. W., Mukhi, N. K., Yarbro, J., Salverda, A. P., Behrens, J. T., Ahn, J., Ma, T., Dhamecha, T. I., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93846-2_90 · Cited by 20 · see citing works
Personalization in OELEs: Developing a Data-Driven Framework to Model and Scaffold SRL Processes
Munshi, A., & Biswas, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-23207-8_65 · Cited by 20 · see citing works
Development and Evaluation of the Oracle - OITS
Aldahdooh, R., & Abu-Naser, S. S. (2017) · PhilPapers (PhilPapers Foundation) · Cited by 19 · see citing works
Feedback in an interactive equation solver
Passier, H. J. M., & Jeuring, J. T. (2006) · Utrecht University Repository (Utrecht University) · Cited by 17 · see citing works
Macro-adaptation in Conversational Intelligent Tutoring Matters
Rus, V., Stefanescu, D. C., Baggett, W. B., Niraula, N. B., Franceschetti, D., & Graesser, A. C. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07221-0_29 · Cited by 14 · see citing works
Using Computer Algebra Systems as Cognitive Tools
Büdenbender, J., Frischauf, A., Giorgi Goguadze, A., Erica Melis, C., Libbrecht, P., & Ullrich, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47987-2_80 · Cited by 12 · see citing works
Agent Prompts: Scaffolding Students for Productive Reflection in an Intelligent Learning Environment
Wu, L., & Looi, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13437-1_92 · Cited by 11 · see citing works
Adaptive Visual Dialog for Intelligent Tutoring Systems
Ahn, J., Chang, M., Watson, P., Tejwani, R., Sundararajan, S. C., Abuelsaad, T., & Prabhu, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93846-2_77 · Cited by 9 · see citing works

Flagged first, then by citations

Research works printed but not linked: 1

[25]Rus, V., Stefanescu, D., Niraula, N., Graesser, A.C.: DeepTutor: towards macro-and micro-adaptive conversational intelligent tutoring at scale. In: Proceedings of the First ACM Conference on Learning@ Scale Conference, pp. 209–210 (2014)

Sorted by citations

Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world
(2023) · doi:10.1007/s11423-023-10203-6 · Cited by 734 · see citing works
NLP techniques for automating responses to customer queries: a systematic review
Olujimi, P. A., & Ade-Ibijola, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00065-5 · Cited by 147 · see citing works
Artificial intelligence-based personalised learning in education: a systematic literature review
Farhood, H., Nyden, M., Beheshti, A., & Müller, S. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00598-x · Cited by 115 · see citing works
The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00820-y · Cited by 122 · see citing works
Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece
Chrysafiadi, K., Virvou, M. K., Tsihrintzis, G. A., & Hatzilygeroudis, I. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11444-3 · Cited by 78 · see citing works
Integrating augmented reality into intelligent tutoring systems to enhance science education outcomes
Ateş, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12970-y · Cited by 78 · see citing works
A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring
Bai, X., & Stede, M. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00323-0 · Cited by 67 · see citing works
Exploring the Intersection of Machine Learning and Big Data: A Survey
Dritsas, E., & Τρίγκα, Μ. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7010013 · Cited by 62 · see citing works
A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system
Dever, D. A., Sonnenfeld, N. A., Wiedbusch, M. D., Schmorrow, S. G., Amon, M. J., & Azevedo, R. (2023) · Metacognition and Learning · doi:10.1007/s11409-023-09346-x · Cited by 60 · see citing works
AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application
Ahn, H. Y. (2024) · Sustainability · doi:10.3390/su16209066 · Cited by 56 · see citing works
Leveraging generative artificial intelligence based on large language models for collaborative learning
Tan, S. C., Chen, W., & Chua, B. L. (2023) · Learning Research and Practice · doi:10.1080/23735082.2023.2258895 · Cited by 55 · see citing works
Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching
Shih, S.-C., Chang, C.-C., Kuo, B., & Huang, Y. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11553-z · Cited by 44 · see citing works
Trends in NLP for personalized learning: LDA and sentiment analysis insights
Yu, J. H., & Chauhan, D. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12988-2 · Cited by 44 · see citing works
Reinforcement Learning in Education: A Systematic Literature Review
Riedmann, A., Schäper, P., & Lugrin, B. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00494-6 · Cited by 42 · see citing works
Human Behavior Analysis: A Comprehensive Survey on Techniques, Applications, Challenges, and Future Directions
Essahraui, S., Lamaakal, I., Maleh, Y., Makkaoui, K. E., Bouami, M. F., Ouahbi, I., El‐Latif, A. A. A., Almousa, M., & Rodrigues, J. J. P. C. (2025) · IEEE Access · doi:10.1109/access.2025.3589938 · Cited by 35 · see citing works
Learning Analytics in the Era of Large Language Models
Mazzullo, E., Bulut, O., Wongvorachan, T., & Tan, B. (2023) · Analytics · doi:10.3390/analytics2040046 · Cited by 34 · see citing works
Performance gains from adaptive eXtended Reality training fueled by artificial intelligence
Stanney, K. M., Archer, J., Skinner, A. D., Horner, C., Hughes, C. L., Brawand, N., Martin, E., Sanchez, S. A., Moralez, L. A., Fidopiastis, C., et al. (2021) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/15485129211064809 · Cited by 34 · see citing works
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Scarlatos, A., Smith, D., Woodhead, S., & Lan, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64302-6_20 · Cited by 32 · see citing works
Investigating the effect of nudges for improving comment quality in active video watching
Mohammadhassan, N., Mitrović, A., & Neshatian, K. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104340 · Cited by 31 · see citing works
Exploring the role of AI in higher education: a natural language processing analysis of emerging trends and discourses
Durón, N. G., & Preciado, A. L. J. (2025) · The TQM Journal · doi:10.1108/tqm-10-2024-0376 · Cited by 29 · see citing works
Using ai in English language learning: An exploration of Cambodian efl university students’ experiences and perceptions
Sol, K., Heng, K., & Sok, S. (2025) · Journal of science of learning and innovations. · doi:10.1163/29497736-bja00015 · Cited by 25 · see citing works
Using Intelligent Tutoring Systems to Enhance Student Engagement
Aarthi, S., Ravikumar, R. N., o'g'li, K. S. S., Ismailov, A., & Khujaniyozova, O. R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1827-1.ch008 · Cited by 24 · see citing works
Student 5.0: immersive learning in next-gen Automation of Manufacturing Systems courses for the Industry 5.0 era
Ramírez-Cedillo, E., Armendáriz-Rodríguez, M., García-Ávila, J., Quintero-Sanchez, D. I., Ortiz, A., & Vargas‐Martínez, A. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1416761 · Cited by 23 · see citing works
AI platform model on 4IR megatrend challenges: complex thinking by active and transformational learning
Sanabria-Z, J., & Olivo-Montaño, P. G. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-07-2023-0145 · Cited by 23 · see citing works
Clustering students’ writing behaviors using keystroke logging: a learning analytic approach in EFL writing
Talebinamvar, M., & Zarrabi, F. (2022) · Language Testing in Asia · doi:10.1186/s40468-021-00150-5 · Cited by 21 · see citing works
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
Grenander, M., Belfer, R., Kochmar, E., Serban, I. V., St-Hilaire, F., & Cheung, J. C. K. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17829 · Cited by 21 · see citing works
Self-citation
Adaptive, Synchronous, and Mobile Online Education: Developing the ASYMPTOTE Learning Environment
Barlovits, S., Caldeira, A., Fesakis, G., Jablonski, S., Koutsomanoli-Filippaki, D., Lázaro, C., Ludwig, M., Mammana, M. F., Moura, A., Oehler, D.-X. K., et al. (2022) · Mathematics · doi:10.3390/math10101628 · Cited by 20 · see citing works
Understanding Student Engagement in AI-Powered Online Learning Platforms
Garcia, M. B., Goi, C. L., Shively, K., Maher, D., Rosak-Szyrocka, J., Happonen, A., Bozkurt, A., & Damaševičius, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5633-3.ch001 · Cited by 19 · see citing works
Optimizing learning outcomes: a deep dive into hybrid AI models for adaptive educational feedback
Qadir, H. M., Suleman, M. T., Khan, R. A., Sohaib, M., Hasan, M. J., & Hussain, S. A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01187-6 · Cited by 18 · see citing works
A feedback model applied in a ubiquitous microlearning environment using SECA rules
Tabares, M. S., Vallejo, P., Montoya, A., & Correa, D. (2022) · Journal of Computing in Higher Education · doi:10.1007/s12528-021-09306-x · Cited by 18 · see citing works
Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems
Anwar, A., Haq, I. U., Mian, I. A., Shah, F., Alroobaea, R., Hussain, S., Ullah, S. S., & Umar, F. (2022) · Mobile Information Systems · doi:10.1155/2022/6006467 · Cited by 18 · see citing works
DNA of learning behaviors: A novel approach of learning performance prediction by NLP
Lin, C.-C., Cheng, E., Huang, A. Y. Q., & Yang, S. J. H. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100227 · Cited by 16 · see citing works
The impact of intelligent tutoring systems and artificial intelligence on students' motivation and achievement in STEM education: A systematic review
Ilić, J., Ivanović, M. D., & Klašnja‐Milićević, A. (2024) · Journal of Educational Studies in Mathematics and Computer Science · doi:10.5937/jesmac2402005i · Cited by 14 · see citing works
AI-Driven Personalization 2.0: Hyper-Personalized Journeys for Every Student Type
Jayaram, Y. (2024) · International Journal of Artificial Intelligence Data Science and Machine Learning · doi:10.63282/3050-9262.ijaidsml-v5i1p114 · Cited by 12 · see citing works
Empowering Teachers With Generative AI Tools and Support
Si̇pahi̇oğlu, M. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1351-0.ch011 · Cited by 12 · see citing works
Understanding Student Engagement in AI-Powered Online Learning Platforms: A Narrative Review of Key Theories and ModelsPreprint
Garcia, M. B., Goi, C. L., Shively, K., Maher, D., Rosak-Szyrocka, J., Happonen, A., Bozkurt, A., & Damaševičius, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5074608 · Cited by 12 · see citing works
From red ink to algorithms: investigating the use of large language models in academic writing feedback
Jovic, M., Papakonstantinidis, S., & Kirkpatrick, R. (2025) · Language Testing in Asia · doi:10.1186/s40468-025-00389-2 · Cited by 11 · see citing works
AI-Powered Personalised Learning Plans for Intelligent Tutoring Systems
Shah, T. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.62879 · Cited by 11 · see citing works
A Survey on Feedback Types in Automated Programming Assessment Systems
Frankford, E., Antensteiner, T., Vierhauser, M., Sauerwein, C., Wallner, V., Groher, I., Plösch, R., & Breu, R. (2026) · ACM Transactions on Computing Education · doi:10.1145/3773911 · Cited by 5 · see citing works
Integrating Artificial Intelligence and Natural Language Processing in E-Learning Platforms: A Review of Opportunities and Limitations
Kaouni, M., Lakrami, F., & Labouidya, O. (2023) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist56084.2023.10409971 · Cited by 8 · see citing works
The AI Co-Pilot: A Culturally-Responsive Framework for Orchestrating Interactive Learning in the Classroom
Zheng, C., & Chen, G. (2025) · International Journal of Chinese Education · doi:10.1177/2212585x251400819 · Cited by 6 · see citing works
AI-assisted university programming education in practice
Johanyák, Z. C., Cserkó, J., & Pásztor, A. (2023) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet58097.2023.00039 · Cited by 6 · see citing works
Navigating Ethical Consideration of Artificial Intelligence and the Metaverse in Scientific Research
Uunona, G. N., & Goosen, L. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0340-6.ch011 · Cited by 5 · see citing works
Question Personalization in an Intelligent Tutoring System
Elkins, S., Kochmar, E., Belfer, R., Serban, I. V., & Cheung, J. C. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11647-6_121 · Cited by 5 · see citing works
Self-citation
AI - Powered Digital Transformation in Higher Education: Intelligent Simulation Tool
Wang, X., Yue, P., & Li, Z. (2025) · doi:10.1109/icaie64856.2025.11158253 · Cited by 4 · see citing works
ADAPTING PEDAGOGICAL SCAFFOLDING FOR DIGITAL NATIVES THROUGH TECHNOLOGY
Hussein, K., Ali, F. D. A., Ne’matullah, K. F., Khairundin, S. A. A., Shamsudin, N. H., & Shanmugam, J. (2025) · International Journal of Modern Education · doi:10.35631/ijmoe.726051 · Cited by 4 · see citing works
Transforming Education with Generative AI: Designing for Post-Prompting Era
Wong, L. H. (2025) · Information and Technology in Education and Learning · doi:10.12937/itel.5.1.inv.p003 · Cited by 4 · see citing works
e-FeeD4Mi: Automating Tailored LA-Informed Feedback in Virtual Learning Environments
Ortega‐Arranz, A., Topali, P., Asensio-Pérez, J. I., Villagrá‐Sobrino, S., Martínez‐Monés, A., & Dimitriadis, Y. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16290-9_39 · Cited by 4 · see citing works
Using an AI-Nurse to Help Teach Graduating Medical Students to Take Overnight Call
Weaver, C., Caputo, N. A., Yost, C. D., & Hauert, S. (2025) · Medical Science Educator · doi:10.1007/s40670-025-02335-6 · Cited by 4 · see citing works
A Comparative Study of Learning Outcomes for Online Learning Platforms
St-Hilaire, F., Burns, N. P., Belfer, R., Shayan, M., Smofsky, A., Vu, D. D., Frau, A., Potochny, J., Faraji, F., Pavero, V., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78270-2_59 · Cited by 4 · see citing works
Self-citation
AI Sensing and Intervention in Higher Education: Student Perceptions of Learning Impacts, Affective Responses, and Ethical Priorities
Han, B., Ma, Y., Coghlan, S., McKay, D., Buchanan, G., & Smith, W. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790360 · Cited by 3 · see citing works
“Mean-while, ITS’s offering personalised automated hints and explanations have been shown to improve learning outcomes and receive positive evaluation from students [76, 130].”
AI-Informed Pedagogy for a Post-Truth Era
Nally, D. (2025) · Digital Society · doi:10.1007/s44206-025-00230-6 · Cited by 3 · see citing works
Comparative analysis of traditional and metaverse-based education: impacts on students’ all-round development
Du, L., Kuo, W. T., Tang, Y. K., & Ho, G. T. S. (2026) · BMC Psychology · doi:10.1186/s40359-025-03914-3 · Cited by 3 · see citing works
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 · see citing works
Piloting Natural Language Generation for Personalized Progress Feedback
Leppänen, L., Hellas, A., & Leinonen, J. (2022) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie56618.2022.9962555 · Cited by 3 · see citing works
Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques
Gratani, F., Screpanti, L., Giannandrea, L., Scaradozzi, D., & Capolla, L. M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-67351-1_30 · Cited by 3 · see citing works
Exploring feedback dynamics: an experimental analysis of human and computer feedback on motivation, engagement, and GRIT in secondary school students
Heindl, L., Huber, V., Schuricht, V., Wawrzinek, D., Babl, U., & Ellert, G. (2025) · Frontiers in Education · doi:10.3389/feduc.2024.1473727 · Cited by 3 · see citing works
Beyond analytics: Using computer‐aided methods in educational research to extend qualitative data analysis
Vieira, C., Ortega-Alvarez, J. D., Magana, A. J., & Boutin, M. (2024) · Computer Applications in Engineering Education · doi:10.1002/cae.22749 · Cited by 3 · see citing works
Personalized Formative Feedbacks and Recommendations Based on Learning Analytics to Enhance the Learning of Java Programming
Hegde, V., Pai, A. R., & Shastry, R. J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-5331-6_67 · Cited by 3 · see citing works
Comprehensive Framework for Artificial Intelligence and Big Data Integration in Highe Education
Kadhem, H. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6400-9_2 · Cited by 2 · see citing works
When AI pushes back: The impact of AI dissent on user knowledge innovation in online knowledge communities
Jiaxuan, L., Han, M., & Yuan, Q. (2026) · Electronic Markets · doi:10.1007/s12525-026-00872-5 · Cited by 2 · see citing works
Simulation training in spinal endoscopic surgery: a systematic review of current status
Garcia, G. P. R., Alotaibi, A., Yousefi, O., Shahbandi, A., Ghamasaee, P., Kassab, A. M. Y., Bokhari, R. F. Y., Abd‐El‐Barr, M. M., Shabani, S., & Bakhaidar, M. (2026) · Neurosurgical Review · doi:10.1007/s10143-026-04191-3 · Cited by 2 · see citing works
AI-Based Interactive Media Creation Training Using Curipod for High School Teachers: Strengthening Digital Literacy
Sulsilah, H., Susanti, E., Rizal, R., & Mahmudah, I. R. (2025) · Jurnal Pengabdian Sains dan Humaniora · doi:10.32938/jpsh.v4i2.10620 · Cited by 2 · see citing works
Artificial Intelligence Based Expert System and Higher Order Thinking Skills: A Brief Review
Shiwani, S., Chaturvedi, D. K., & Singh, M. (2025) · International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) · doi:10.1109/ic3ecsbhi63591.2025.10991214 · Cited by 1 · see citing works
Learning Code-Edit Embeddings to Model Student Debugging Behavior
Heickal, H., & Lan, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98465-5_12 · Cited by 1 · see citing works
Can AI Universally Help Students and Teachers?: A Cross-Cultural Investigation of Perceptions of AI's Potential in Education
Mekadenaumporn, N. (2025) · Journal of Education Research · doi:10.37985/jer.v6i4.2846 · Cited by 1 · see citing works
Eğitimde Yapay Zekâ: Biçimlendirici Değerlendirme ile Denge Arayışı
Arslan, K., & Arslan, K. (2025) · Batı anadolu eğitim bilimleri dergisi · doi:10.51460/baebd.1757476 · Cited by 1 · see citing works
Designing conversational Agents for adaptive instructional support in business simulation gaming
Wenzel, A., Geiger, J.-M., & Liening, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100576 · Cited by 1 · see citing works
Should the AI Speak First? Evaluating Proactive vs. Reactive Facilitation in Mixed-Reality Medical Training
Wang, D., Song, W., Ni, J., Zheng, Q., Zhou, Y., Liang, K., Yao, M. Z., & Cao, C. G. L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791910 · Cited by 1 · see citing works
Usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review
Septo, J. S., Dinopol, J., Reyes, T. J. M. D., Santiago, C. S., & Cahapin, E. L. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-0147-9_18 · Cited by 1 · see citing works
SmolLab_SEU at BEA 2025 Shared Task: A Transformer-Based Framework for Multi-Track Pedagogical Evaluation of AI-Powered Tutors
Rahman, M. A., Amin, A., Aftahee, S., Junayed, M., & Rahman, M. A. (2025) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2025.bea-1.88 · Cited by 1 · see citing works
Students' 21st-Century Economic Competencies in The Digital Age : Exploring The Interplay Between Artificial Intelligence, Augmented Reality, and Learning Engagement
Winokan, A. N., Arsana, I. K. S., Dame, J. M., & Dolonseda, H. P. (2025) · Jurnal Kependidikan Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan Pengajaran dan Pembelajaran · doi:10.33394/jk.v11i3.16875 · Cited by 1 · see citing works
Investigating the Use of a Web-based Application on Geometric Proof Understanding: A Study of Individual and Small-Group Learning in Junior High School
Hamada, S., Geng, X., Chen, L., & Yamada, M. (2025) · Information and Technology in Education and Learning · doi:10.12937/itel.5.1.pra.p001 · Cited by 1 · see citing works
Integrating AI and Data Analytics: Transforming Business Education for the Digital Age
Chandola, V., Gupta, P., & Joshi, H. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80592-911-620261014 · Cited by 1 · see citing works
The Interplay Between In-Game Activity, Learning Gains, and Enjoyment in a Serious Game on STEM
Ruipérez‐Valiente, J. A. (2022) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-7998-9732-3.ch002
A Case Study of Teachers’ First Designs Using ASYMPTOTE: A Tool for Teaching and Learning Mathematics Online
Mammana, M. F., Taranto, E., Koutsomanoli-Filippaki, D., & Fesakis, G. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-29800-4_44
Assisting Instructors in the Design and Provision of Personalized LA-informed Feedback in MOOCs
Topali, P. (2023) · doi:10.35376/10324/61779
ФОРМУВАЛЬНЕ ОЦІНЮВАННЯ ЯК ОСНОВА СТРУКТУРУВАННЯ ПІДРУЧНИКІВ «ТЕХНОЛОГІЇ» ДЛЯ БАЗОВОЇ СЕРЕДНЬОЇ ОСВІТИ
Мачача, Т. (2024) · Problems of Modern Textbook · doi:10.32405/2411-1309-2024-32-105-121
AI based Learning in Biology
Shiwani, & Chaturvedi, D. K. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3944-2.ch007
Didattica e Prompt Engineering: una nuova competenza digitale per i docenti nell'era dell'Intelligenza Artificiale Generativa
Baldassarre, M., Cuzzi, A. M., & Sarcina, F. P. (2025) · EDUCATION SCIENCES AND SOCIETY · doi:10.3280/ess2-2024oa18443
Development and Evaluation of Intelligent Tutoring System for Chinese Language Teaching Based on Natural Language Processing
Pan, Y. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.1171
Roles Of Artificial Intelligence in Education
Dr. Ahmed Alshatir, M. E. (2025) · Humanitarian and Natural Sciences Journal · doi:10.53796/hnsj67/33
Einführung
Hilbert, S., Kraus, E., & Lindl, A. (2025) · Springer eBooks · doi:10.1007/978-3-658-43649-0_1
Integrated Application of Augmented Reality and Artificial Intelligence into English Learning
Zhang, Q. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-95252-4_22
Human and Artificial Agent Interaction to Provide Generative Feedback at University
Giannandrea, L., Gratani, F., Laici, C., Capolla, L. M., Rossi, P. G., Scaradozzi, D., & Screpanti, L. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94002-6_13
A Bibliometric Analysis of Visualizing Knowledge Mapping for Intelligent Tutoring Systems
Ye, X., Wang, J., & Qin, Q. (2025) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett66247.2025.11137303
Pengembangan Model Pembelajaran Berbasis Kecerdasan Buatan untuk Penilaian Esai Reflektif dan Pemberian Umpan Balik Adaptif bagi Mahasiswa PPG
Hidayat, R., Suharsono, N., & Hari, M. (2025) · Al-Kharaj Jurnal Ekonomi Keuangan & Bisnis Syariah · doi:10.47467/alkharaj.v7i10.9385
IMPACT OF ARTIFICIAL INTELLIGENCE ON THE EDUCATION LANDSCAPE IN INDIA: BRIDGING THE RURAL–URBAN DIVIDE
Sriram, A. V. (2025) · doi:10.34218/ijaird_03_02_009
Empowering Students: Utilizing Cambridge's Write and Improve for Personalized Writing Development
Sihite, M. R., Ekayati, R., Nababan, I. A., & Murni, S. (2025) · Conference on English Language Teaching · doi:10.24090/celti.2025.1323
Comparative Analysis of Generative AI Performance in University Programming Courses
Suomalainen, E., Vanhala, E., & Ikonen, J. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-14518-5_16
Revolutionizing Mathematics Education With AI-Based Learning Tools
Bhatia, M., Gocher, H., Verma, G., Singh, Y., & Sharma, D. V. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9225-7.ch005
Sechs Engagement-Typen in Mathematischen Lehrveranstaltungen an der Universität
Weich, M., Stalder, B. E., & Templer, F. (2025) · Swiss Journal of Educational Research · doi:10.24452/sjer.47.3.7
CodeMentor-AI: AI-Powered Feedback Application for Programming in Open and Distance Learning
Subramanıam, N. K. (2026) · Journal of Learning for Development · doi:10.56059/jl4d.v13i1.1882
Personalised Educational Scaffolding
Spiteri, B. J., & Dingli, A. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-4423-3_15
Développer la flexibilité cognitive à l'école : Étude des effets d'entraînements à la catégorisation flexible en résolution de problèmes et en conjugaison
Iacono, É. (2024) · doi:10.70675/4554f9beza60fz4e7dza6cez826df2e98057
Development and Application of an Intelligent Tutoring System for Higher Education Based on an Educational Knowledge Graph
Jian, X., & Li, N. (2026) · International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508949
Is Generative AI an Illegal Crutch or Tutor in Generation Z's Learning Processes?
Sterczl, G. R. (2026) · IEEE International Conference on Computational Cybernetics (ICCC) · doi:10.1109/iccc68994.2026.11511589
Using Artificial Intelligence to Create a Web Application Security Knowledge Assessment System
Čović, Z., Vučinić, F., & Fürstner, I. (2026) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-032-14058-6_12
AI-Powered Personalized Learning System using LLMs
(2026) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets99162
Personalized AI Tutor An Intelligent Adaptive Learning System for Early Education
Varalakshmi, M. V. A., Maniteja, A., Shaik, N., & Eslavath, P. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2026.150500078

Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00267-x · Cited by 112 · see citing works
Self-citation
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor SystemsPreprint
Grenander, M., Belfer, R., Kochmar, E., Serban, I. V., St-Hilaire, F., & Cheung, J. C. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07785 · Cited by 2 · see citing works
Self-citation
Data Mining to Generate Individualised Feedback
Dominguez, A. K., Yacef, K., & Curran, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13437-1_52 · Cited by 12 · see citing works
Automated Feedback Generation for Learner Modeling in Intelligent Tutoring Systems
Lu, C. H. (2021) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-0dg5-9n58 · Cited by 2 · see citing works
Enhancing Automated Feedback in Intelligent Tutoring Systems via Large Language Models and Data Distillation
Meng, X., & Yang, Y. (2025) · International Conference on Information and Education Technology (ICIET) · doi:10.1109/iciet66371.2025.11046219

Authors: Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J.
Affiliations: University of Cambridge (GB)
École de Technologie Supérieure (CA)
Corresponding author: Kochmar, Ekaterina
ORCID: Kochmar, Ekaterina
Gupta, Varun
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2020
Pages: 140-146
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-52240-7_26
PMC: PMC7334734
OpenAlex: W3023718816
Cited by: 103
References: 24
Citations by year: 2026: 31 · 2025: 30 · 2024: 17 · 2023: 10 · 2022: 11 · 2021: 4
FWCI: 2.39
Type: conference paper
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
PubMed Central (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords: intelligent tutoring systems, personalized feedback, automated feedback, natural language generation, learning outcomes

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks