Navigating Digital Classrooms As Educators—Are We Ready to Adapt?

Authors: (1) Florian Gnadlinger, Faculty of Computer Science, Communication, and Economics, University of Applied Sciences Berlin, Germany; (2) Simone Kriglstein, Faculty of Informatics, Masaryk University, Czech Republic. Table of Links Abstract and 1 Introduction 2 Background 3 Methode & Results 4 Discussion 5 Conclusion and References 3 METHODE & RESULTS The presumption to the given research question (RQ1) is that educators are only able to use the full potential of intelligent tutoring systems if they (1) have access to the information obtained from the learners, (2) are able to understand and interpret this information, (3) and can transform this interpretation into valuable pedagogical and didactical actions. This aligns with the learning analytics process model [31] (see Figure 2), which is applicable to learner or teaching dashboards and authoring interfaces. Hence, regarding the results from an ongoing systematic literature review, we concluded a revised meta-architecture of intelligent tutoring systems that incorporate the role of educators (Figure 1 black elements). This draws attention to the design of teaching dashboards, allowing views into and interactions with the different models of such systems (compare with Figure 1). Besides static dashboards, a functional entity is needed to support the educators´ reflection process about the effectiveness of their teaching methods. We call this entity educator model. 4 DISCUSSION With the given summary, the illustrated current systematic overview (Figure 1 gray elements), and a visualized extension proposal (Figure 1 black elements), we would like to point out a major implication for teachers in higher education when introducing intelligent tutoring systems into their educational setting. If teachers in higher education are using or will start using intelligent tutoring systems, they should reflect on three main questions. (1) Do I have access to all information incorporated into the different models of intelligent tutoring systems? (2) I am able to understand and interpret this information? (3) I am able to transform this interpretation into pedagogical or didactical actions? 5 CONCLUSION With this contribution, we hope to give higher education teachers some leverage to participate in the discussion of the design of intelligent tutoring systems. REFERENCES Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022): The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. doi:10.1007/s11528-022-00715-y Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., Wasson, B. (2023): Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning. doi:10.1111/jcal.12844 Pishtari, G., Ley, T., Khalil, M., Kasepalu, R., Tuvi, I. (2023): Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review. Education Sciences. doi:10.3390/educsci13050498 The Glossary of Education Reform (2014): Competency-Based Learning. https://www.edglossary.org/competencybased-learning/. Accessed 14 December 2023 Alt, D., Nirit, R. (2018): Lifelong Citizenship. Lifelong Learning as a Lever for Moral and Democratic Values. Moral Development and Citizenship Education, vol. 13. Brill/Sense, Leiden, the Netherlands. doi:10.1163/9789463512398. 978-94-6351- 239-8 Alt, D., Naamati-Schneider, L., Weishut, D.J. (2023): Competency-based learning and formative assessment feedback as precursors of college students’ soft skills acquisition. Studies in Higher Education. doi:10.1080/03075079.2023.2217203 Levine, E., Patrick, S. (2019): What is competency-based education? An updated definition. https://files.eric.ed.gov/fulltext/ED604019.pdf. Accessed 14 December 2023 Ghaicha, A. (2016): Theoretical Framework for Educational Assessment: A Synoptic Review. Journal of Education and Practice 7, 212–231 Rust, C. (2002): Purposes and Principles of Assessment. Learning and Teaching Briefing Papers Series Curry, R.A., Gonzalez-DeJesus, N.T. (2010): A Literature Review of Assessment. Journal of Diagnostic Medical Sonography. doi:10.1177/8756479310361374 Machin, L. (2016): A complete guide to the level 5 diploma in education and training. Further Education. Critical Publishing Ltd, Northwich. 978-1910391785 Schildkamp, K., van der Kleij, F.M., Heitink, M.C., Kippers, W.B., Veldkamp, B.P. (2020): Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research. doi:10.1016/j.ijer.2020.101602 Spatioti, A.G., Kazanidis, I., Pange, J. (2022): A Comparative Study of the ADDIE Instructional Design Model in Distance Education. Information. doi:10.3390/info13090402 Frerejean, J., van Merriënboer, J.J., Kirschner, P.A., Roex, A., Aertgeerts, B., Marcellis, M. (2019): Designing instruction for complex learning: 4C/ID in higher education. Euro J of Education. doi:10.1111/ejed.12363 Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R. (eds.) (2012): Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining 4(1). doi:10.5281/zenodo.3554641 Gnadlinger, F., Selmanagić, A., Simbeck, K., Kriglstein, S. (2023): Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In: Jovanovic, J., Chounta, I.-A., Uhomoibhi, J., McLaren, B. (eds.) Proceedings of the 15th International Conference on Computer Supported Education. 15th International Conference on Computer Supported Education, Prague, Czech Republic, 21.04.2023 - 23.04.2023, pp. 272–280. SCITEPRESS. doi:10.5220/0011964700003470 Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D., Williamson, D.M. (2015): Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. doi:10.1007/978-1- 4939-2125-6. 9781493921256 Shute, V.J., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., Sun, C. (2021): Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. J Comput Assist Learn. doi:10.1111/jcal.12473 Bryant, J., Heitz, C., Sanghvi, S., Wagle, D. (2020): How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/. Accessed 9 February 2024 Hughes, J. (2022): Deskilling of Teaching and the Case for Intelligent Tutoring Systems. J. Eth. Emerg. Tech. doi:10.55613/jeet.v31i2.90 Vanbecelaere, S., van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., Depaepe, F. (2020): The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning. doi:10.1111/jcal.12416 Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A. (2020): Online Adaptive Learning. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. SITA'20: Theories and Applications, Rabat Morocco, 23 09 2020 24 09 2020, pp. 1–6. ACM, New York, NY, USA. doi:10.1145/3419604.3419759 Gao, Y. (2023): The Potential of Adaptive Learning Systems to Enhance Learning Outcomes: A Meta-Analysis. doi:10.7939/r3-a6xdm403 Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011): Recommender Systems in Technology Enhanced Learning. In: Ricci, F. (ed.) Recommender systems handbook, pp. 387–415. Springer, New York. doi:10.1007/978-0-387-85820-3_12 Ricci, F., Rokach, L., Shapira, B. (eds.) (2015): Recommender Systems Handbook. Springer, New York. doi:10.1007/978-1-4899- 7637-6. 978-1-4899-7636-9 Ramadhan, A., Warnars, H.L.H.S., Razak, F.H.A. (2023): Combining intelligent tutoring systems and gamification: a systematic literature review. Educ Inf Technol. doi:10.1007/s10639-023-12092-x Kurni, M. (2023): A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 1st edn. Springer International Publishing; Springer, Cham. 978-3-031-32653-0 Abdelbaset R. Almasri, Adel Ahmed, Naser Al-Masri, Yousef S. Abu Sultan, Ahmed Y. Mahmoud, Ihab Zaqout, Alaa N. Akkila, Samy S. Abu-Naser (2019): Intelligent Tutoring Systems Survey for the Period 2000- 2018. International Journal of Academic Engineering Research (IJAER) vol. 3, 21-37 Woolf, B.P. (2010): Building Intelligent Interactive Tutors. Elsevier Science. 978-0-08- 092004-7 Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D. (2018): Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature. Int J Artif Intell Educ. doi:10.1007/s40593- 017-0157-9 Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L. (2013): Learning Analytics Dashboard Applications. American Behavioral Scientist. doi:10.1177/0002764213479363 This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Florian Gnadlinger, Faculty of Computer Science, Communication, and Economics, University of Applied Sciences Berlin, Germany; (2) Simone Kriglstein, Faculty of Informatics, Masaryk University, Czech Republic. Authors: Authors: (1) Florian Gnadlinger, Faculty of Computer Science, Communication, and Economics, University of Applied Sciences Berlin, Germany; (2) Simone Kriglstein, Faculty of Informatics, Masaryk University, Czech Republic. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Methode & Results 3 Methode & Results 4 Discussion 4 Discussion 5 Conclusion and References 5 Conclusion and References 3 METHODE & RESULTS The presumption to the given research question (RQ1) is that educators are only able to use the full potential of intelligent tutoring systems if they (1) have access to the information obtained from the learners, (2) are able to understand and interpret this information, (3) and can transform this interpretation into valuable pedagogical and didactical actions. This aligns with the learning analytics process model [31] (see Figure 2), which is applicable to learner or teaching dashboards and authoring interfaces. Hence, regarding the results from an ongoing systematic literature review, we concluded a revised meta-architecture of intelligent tutoring systems that incorporate the role of educators (Figure 1 black elements). This draws attention to the design of teaching dashboards, allowing views into and interactions with the different models of such systems (compare with Figure 1). Besides static dashboards, a functional entity is needed to support the educators´ reflection process about the effectiveness of their teaching methods. We call this entity educator model. 4 DISCUSSION With the given summary, the illustrated current systematic overview (Figure 1 gray elements), and a visualized extension proposal (Figure 1 black elements), we would like to point out a major implication for teachers in higher education when introducing intelligent tutoring systems into their educational setting. If teachers in higher education are using or will start using intelligent tutoring systems, they should reflect on three main questions. (1) Do I have access to all information incorporated into the different models of intelligent tutoring systems? (2) I am able to understand and interpret this information? (3) I am able to transform this interpretation into pedagogical or didactical actions? 5 CONCLUSION With this contribution, we hope to give higher education teachers some leverage to participate in the discussion of the design of intelligent tutoring systems. REFERENCES Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022): The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. doi:10.1007/s11528-022-00715-y Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., Wasson, B. (2023): Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning. doi:10.1111/jcal.12844 Pishtari, G., Ley, T., Khalil, M., Kasepalu, R., Tuvi, I. (2023): Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review. Education Sciences. doi:10.3390/educsci13050498 The Glossary of Education Reform (2014): Competency-Based Learning. https://www.edglossary.org/competencybased-learning/. Accessed 14 December 2023 Alt, D., Nirit, R. (2018): Lifelong Citizenship. Lifelong Learning as a Lever for Moral and Democratic Values. Moral Development and Citizenship Education, vol. 13. Brill/Sense, Leiden, the Netherlands. doi:10.1163/9789463512398. 978-94-6351- 239-8 Alt, D., Naamati-Schneider, L., Weishut, D.J. (2023): Competency-based learning and formative assessment feedback as precursors of college students’ soft skills acquisition. Studies in Higher Education. doi:10.1080/03075079.2023.2217203 Levine, E., Patrick, S. (2019): What is competency-based education? An updated definition. https://files.eric.ed.gov/fulltext/ED604019.pdf. Accessed 14 December 2023 Ghaicha, A. (2016): Theoretical Framework for Educational Assessment: A Synoptic Review. Journal of Education and Practice 7, 212–231 Rust, C. (2002): Purposes and Principles of Assessment. Learning and Teaching Briefing Papers Series Curry, R.A., Gonzalez-DeJesus, N.T. (2010): A Literature Review of Assessment. Journal of Diagnostic Medical Sonography. doi:10.1177/8756479310361374 Machin, L. (2016): A complete guide to the level 5 diploma in education and training. Further Education. Critical Publishing Ltd, Northwich. 978-1910391785 Schildkamp, K., van der Kleij, F.M., Heitink, M.C., Kippers, W.B., Veldkamp, B.P. (2020): Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research. doi:10.1016/j.ijer.2020.101602 Spatioti, A.G., Kazanidis, I., Pange, J. (2022): A Comparative Study of the ADDIE Instructional Design Model in Distance Education. Information. doi:10.3390/info13090402 Frerejean, J., van Merriënboer, J.J., Kirschner, P.A., Roex, A., Aertgeerts, B., Marcellis, M. (2019): Designing instruction for complex learning: 4C/ID in higher education. Euro J of Education. doi:10.1111/ejed.12363 Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R. (eds.) (2012): Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining 4(1). doi:10.5281/zenodo.3554641 Gnadlinger, F., Selmanagić, A., Simbeck, K., Kriglstein, S. (2023): Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In: Jovanovic, J., Chounta, I.-A., Uhomoibhi, J., McLaren, B. (eds.) Proceedings of the 15th International Conference on Computer Supported Education. 15th International Conference on Computer Supported Education, Prague, Czech Republic, 21.04.2023 - 23.04.2023, pp. 272–280. SCITEPRESS. doi:10.5220/0011964700003470 Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D., Williamson, D.M. (2015): Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. doi:10.1007/978-1- 4939-2125-6. 9781493921256 Shute, V.J., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., Sun, C. (2021): Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. J Comput Assist Learn. doi:10.1111/jcal.12473 Bryant, J., Heitz, C., Sanghvi, S., Wagle, D. (2020): How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/. Accessed 9 February 2024 Hughes, J. (2022): Deskilling of Teaching and the Case for Intelligent Tutoring Systems. J. Eth. Emerg. Tech. doi:10.55613/jeet.v31i2.90 Vanbecelaere, S., van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., Depaepe, F. (2020): The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning. doi:10.1111/jcal.12416 Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A. (2020): Online Adaptive Learning. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. SITA'20: Theories and Applications, Rabat Morocco, 23 09 2020 24 09 2020, pp. 1–6. ACM, New York, NY, USA. doi:10.1145/3419604.3419759 Gao, Y. (2023): The Potential of Adaptive Learning Systems to Enhance Learning Outcomes: A Meta-Analysis. doi:10.7939/r3-a6xdm403 Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011): Recommender Systems in Technology Enhanced Learning. In: Ricci, F. (ed.) Recommender systems handbook, pp. 387–415. Springer, New York. doi:10.1007/978-0-387-85820-3_12 Ricci, F., Rokach, L., Shapira, B. (eds.) (2015): Recommender Systems Handbook. Springer, New York. doi:10.1007/978-1-4899- 7637-6. 978-1-4899-7636-9 Ramadhan, A., Warnars, H.L.H.S., Razak, F.H.A. (2023): Combining intelligent tutoring systems and gamification: a systematic literature review. Educ Inf Technol. doi:10.1007/s10639-023-12092-x Kurni, M. (2023): A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 1st edn. Springer International Publishing; Springer, Cham. 978-3-031-32653-0 Abdelbaset R. Almasri, Adel Ahmed, Naser Al-Masri, Yousef S. Abu Sultan, Ahmed Y. Mahmoud, Ihab Zaqout, Alaa N. Akkila, Samy S. Abu-Naser (2019): Intelligent Tutoring Systems Survey for the Period 2000- 2018. International Journal of Academic Engineering Research (IJAER) vol. 3, 21-37 Woolf, B.P. (2010): Building Intelligent Interactive Tutors. Elsevier Science. 978-0-08- 092004-7 Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D. (2018): Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature. Int J Artif Intell Educ. doi:10.1007/s40593- 017-0157-9 Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L. (2013): Learning Analytics Dashboard Applications. American Behavioral Scientist. doi:10.1177/0002764213479363 Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022): The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. doi:10.1007/s11528-022-00715-y Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022): The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends. doi:10.1007/s11528-022-00715-y Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., Wasson, B. (2023): Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning. doi:10.1111/jcal.12844 Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., Wasson, B. (2023): Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analytics. Journal of Computer Assisted Learning. doi:10.1111/jcal.12844 Pishtari, G., Ley, T., Khalil, M., Kasepalu, R., Tuvi, I. (2023): Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review. Education Sciences. doi:10.3390/educsci13050498 Pishtari, G., Ley, T., Khalil, M., Kasepalu, R., Tuvi, I. (2023): Model-Based Learning Analytics for a Partnership of Teachers and Intelligent Systems: A Bibliometric Systematic Review. Education Sciences. doi:10.3390/educsci13050498 The Glossary of Education Reform (2014): Competency-Based Learning. https://www.edglossary.org/competencybased-learning/. Accessed 14 December 2023 The Glossary of Education Reform (2014): Competency-Based Learning. https://www.edglossary.org/competencybased-learning/ . Accessed 14 December 2023 https://www.edglossary.org/competencybased-learning/ Alt, D., Nirit, R. (2018): Lifelong Citizenship. Lifelong Learning as a Lever for Moral and Democratic Values. Moral Development and Citizenship Education, vol. 13. Brill/Sense, Leiden, the Netherlands. doi:10.1163/9789463512398. 978-94-6351- 239-8 Alt, D., Nirit, R. (2018): Lifelong Citizenship. Lifelong Learning as a Lever for Moral and Democratic Values. Moral Development and Citizenship Education, vol. 13. Brill/Sense, Leiden, the Netherlands. doi:10.1163/9789463512398. 978-94-6351- 239-8 Alt, D., Naamati-Schneider, L., Weishut, D.J. (2023): Competency-based learning and formative assessment feedback as precursors of college students’ soft skills acquisition. Studies in Higher Education. doi:10.1080/03075079.2023.2217203 Alt, D., Naamati-Schneider, L., Weishut, D.J. (2023): Competency-based learning and formative assessment feedback as precursors of college students’ soft skills acquisition. Studies in Higher Education. doi:10.1080/03075079.2023.2217203 Levine, E., Patrick, S. (2019): What is competency-based education? An updated definition. https://files.eric.ed.gov/fulltext/ED604019.pdf. Accessed 14 December 2023 Levine, E., Patrick, S. (2019): What is competency-based education? An updated definition. https://files.eric.ed.gov/fulltext/ED604019.pdf . Accessed 14 December 2023 https://files.eric.ed.gov/fulltext/ED604019.pdf Ghaicha, A. (2016): Theoretical Framework for Educational Assessment: A Synoptic Review. Journal of Education and Practice 7, 212–231 Ghaicha, A. (2016): Theoretical Framework for Educational Assessment: A Synoptic Review. Journal of Education and Practice 7, 212–231 Rust, C. (2002): Purposes and Principles of Assessment. Learning and Teaching Briefing Papers Series Rust, C. (2002): Purposes and Principles of Assessment. Learning and Teaching Briefing Papers Series Curry, R.A., Gonzalez-DeJesus, N.T. (2010): A Literature Review of Assessment. Journal of Diagnostic Medical Sonography. doi:10.1177/8756479310361374 Curry, R.A., Gonzalez-DeJesus, N.T. (2010): A Literature Review of Assessment. Journal of Diagnostic Medical Sonography. doi:10.1177/8756479310361374 Machin, L. (2016): A complete guide to the level 5 diploma in education and training. Further Education. Critical Publishing Ltd, Northwich. 978-1910391785 Machin, L. (2016): A complete guide to the level 5 diploma in education and training. Further Education. Critical Publishing Ltd, Northwich. 978-1910391785 Schildkamp, K., van der Kleij, F.M., Heitink, M.C., Kippers, W.B., Veldkamp, B.P. (2020): Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research. doi:10.1016/j.ijer.2020.101602 Schildkamp, K., van der Kleij, F.M., Heitink, M.C., Kippers, W.B., Veldkamp, B.P. (2020): Formative assessment: A systematic review of critical teacher prerequisites for classroom practice. International Journal of Educational Research. doi:10.1016/j.ijer.2020.101602 Spatioti, A.G., Kazanidis, I., Pange, J. (2022): A Comparative Study of the ADDIE Instructional Design Model in Distance Education. Information. doi:10.3390/info13090402 Spatioti, A.G., Kazanidis, I., Pange, J. (2022): A Comparative Study of the ADDIE Instructional Design Model in Distance Education. Information. doi:10.3390/info13090402 Frerejean, J., van Merriënboer, J.J., Kirschner, P.A., Roex, A., Aertgeerts, B., Marcellis, M. (2019): Designing instruction for complex learning: 4C/ID in higher education. Euro J of Education. doi:10.1111/ejed.12363 Frerejean, J., van Merriënboer, J.J., Kirschner, P.A., Roex, A., Aertgeerts, B., Marcellis, M. (2019): Designing instruction for complex learning: 4C/ID in higher education. Euro J of Education. doi:10.1111/ejed.12363 Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R. (eds.) (2012): Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining 4(1). doi:10.5281/zenodo.3554641 Mislevy, R.J., Behrens, J.T., Dicerbo, K.E., Levy, R. (eds.) (2012): Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining 4(1). doi:10.5281/zenodo.3554641 Gnadlinger, F., Selmanagić, A., Simbeck, K., Kriglstein, S. (2023): Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In: Jovanovic, J., Chounta, I.-A., Uhomoibhi, J., McLaren, B. (eds.) Proceedings of the 15th International Conference on Computer Supported Education. 15th International Conference on Computer Supported Education, Prague, Czech Republic, 21.04.2023 - 23.04.2023, pp. 272–280. SCITEPRESS. doi:10.5220/0011964700003470 Gnadlinger, F., Selmanagić, A., Simbeck, K., Kriglstein, S. (2023): Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks. In: Jovanovic, J., Chounta, I.-A., Uhomoibhi, J., McLaren, B. (eds.) Proceedings of the 15th International Conference on Computer Supported Education. 15th International Conference on Computer Supported Education, Prague, Czech Republic, 21.04.2023 - 23.04.2023, pp. 272–280. SCITEPRESS. doi:10.5220/0011964700003470 Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D., Williamson, D.M. (2015): Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. doi:10.1007/978-1- 4939-2125-6. 9781493921256 Almond, R.G., Mislevy, R.J., Steinberg, L.S., Yan, D., Williamson, D.M. (2015): Bayesian Networks in Educational Assessment. Statistics for Social and Behavioral Sciences. Springer, New York, NY. doi:10.1007/978-1- 4939-2125-6. 9781493921256 Shute, V.J., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., Sun, C. (2021): Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. J Comput Assist Learn. doi:10.1111/jcal.12473 Shute, V.J., Rahimi, S., Smith, G., Ke, F., Almond, R., Dai, C.-P., Kuba, R., Liu, Z., Yang, X., Sun, C. (2021): Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games. J Comput Assist Learn. doi:10.1111/jcal.12473 Bryant, J., Heitz, C., Sanghvi, S., Wagle, D. (2020): How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/. Accessed 9 February 2024 Bryant, J., Heitz, C., Sanghvi, S., Wagle, D. (2020): How artificial intelligence will impact K–12 teachers. https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/ . Accessed 9 February 2024 https://www.mckinsey.com/industries/education/our-insights/how-artificialintelligence-will-impact-k-12-teachers#/ Hughes, J. (2022): Deskilling of Teaching and the Case for Intelligent Tutoring Systems. J. Eth. Emerg. Tech. doi:10.55613/jeet.v31i2.90 Hughes, J. (2022): Deskilling of Teaching and the Case for Intelligent Tutoring Systems. J. Eth. Emerg. Tech. doi:10.55613/jeet.v31i2.90 Vanbecelaere, S., van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., Depaepe, F. (2020): The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning. doi:10.1111/jcal.12416 Vanbecelaere, S., van den Berghe, K., Cornillie, F., Sasanguie, D., Reynvoet, B., Depaepe, F. (2020): The effectiveness of adaptive versus non‐adaptive learning with digital educational games. Journal of Computer Assisted Learning. doi:10.1111/jcal.12416 Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A. (2020): Online Adaptive Learning. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. SITA'20: Theories and Applications, Rabat Morocco, 23 09 2020 24 09 2020, pp. 1–6. ACM, New York, NY, USA. doi:10.1145/3419604.3419759 Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A. (2020): Online Adaptive Learning. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. SITA'20: Theories and Applications, Rabat Morocco, 23 09 2020 24 09 2020, pp. 1–6. ACM, New York, NY, USA. doi:10.1145/3419604.3419759 Gao, Y. (2023): The Potential of Adaptive Learning Systems to Enhance Learning Outcomes: A Meta-Analysis. doi:10.7939/r3-a6xdm403 Gao, Y. (2023): The Potential of Adaptive Learning Systems to Enhance Learning Outcomes: A Meta-Analysis. doi:10.7939/r3-a6xdm403 Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011): Recommender Systems in Technology Enhanced Learning. In: Ricci, F. (ed.) Recommender systems handbook, pp. 387–415. Springer, New York. doi:10.1007/978-0-387-85820-3_12 Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R. (2011): Recommender Systems in Technology Enhanced Learning. In: Ricci, F. (ed.) Recommender systems handbook, pp. 387–415. Springer, New York. doi:10.1007/978-0-387-85820-3_12 Ricci, F., Rokach, L., Shapira, B. (eds.) (2015): Recommender Systems Handbook. Springer, New York. doi:10.1007/978-1-4899- 7637-6. 978-1-4899-7636-9 Ricci, F., Rokach, L., Shapira, B. (eds.) (2015): Recommender Systems Handbook. Springer, New York. doi:10.1007/978-1-4899- 7637-6. 978-1-4899-7636-9 Ramadhan, A., Warnars, H.L.H.S., Razak, F.H.A. (2023): Combining intelligent tutoring systems and gamification: a systematic literature review. Educ Inf Technol. doi:10.1007/s10639-023-12092-x Ramadhan, A., Warnars, H.L.H.S., Razak, F.H.A. (2023): Combining intelligent tutoring systems and gamification: a systematic literature review. Educ Inf Technol. doi:10.1007/s10639-023-12092-x Kurni, M. (2023): A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 1st edn. Springer International Publishing; Springer, Cham. 978-3-031-32653-0 Kurni, M. (2023): A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, 1st edn. Springer International Publishing; Springer, Cham. 978-3-031-32653-0 Abdelbaset R. Almasri, Adel Ahmed, Naser Al-Masri, Yousef S. Abu Sultan, Ahmed Y. Mahmoud, Ihab Zaqout, Alaa N. Akkila, Samy S. Abu-Naser (2019): Intelligent Tutoring Systems Survey for the Period 2000- 2018. International Journal of Academic Engineering Research (IJAER) vol. 3, 21-37 Abdelbaset R. Almasri, Adel Ahmed, Naser Al-Masri, Yousef S. Abu Sultan, Ahmed Y. Mahmoud, Ihab Zaqout, Alaa N. Akkila, Samy S. Abu-Naser (2019): Intelligent Tutoring Systems Survey for the Period 2000- 2018. International Journal of Academic Engineering Research (IJAER) vol. 3, 21-37 Woolf, B.P. (2010): Building Intelligent Interactive Tutors. Elsevier Science. 978-0-08- 092004-7 Woolf, B.P. (2010): Building Intelligent Interactive Tutors. Elsevier Science. 978-0-08- 092004-7 Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D. (2018): Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature. Int J Artif Intell Educ. doi:10.1007/s40593- 017-0157-9 Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D. (2018): Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature. Int J Artif Intell Educ. doi:10.1007/s40593- 017-0157-9 Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L. (2013): Learning Analytics Dashboard Applications. American Behavioral Scientist. doi:10.1177/0002764213479363 Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L. (2013): Learning Analytics Dashboard Applications. American Behavioral Scientist. doi:10.1177/0002764213479363 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv