PreprintPDF Available

Bildungstechnologie-Design von KI-gestützten Avataren zur Förderung selbstregulierten Lernens

Authors:
Sandra Hummel
Sandra Hummel
Sandra Hummel
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Mana-Teresa Donner at TUD Dresden University of Technology
Mana-Teresa Donner
Syed Hur Abbas at TUD Dresden University of Technology
Syed Hur Abbas
Gitanjali Wadhwa
Gitanjali Wadhwa
Gitanjali Wadhwa
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Preprints and early-stage research may not have been peer reviewed yet.
Download file PDF
Read file
Download citation
Content uploaded by Mana-Teresa Donner
Author content
All content in this area was uploaded by Mana-Teresa Donner on Mar 12, 2025
Content may be subject to copyright.
A.1 Bildungstechnologie-Design von KI-gestützten
Avataren zur Förderung selbstregulierten
Lernens
Sandra Hummel1, Mana-Teresa Donner1, Syed Hur Abbas1,
Gitanjali Wadhwa1
1 TUD Dresden University of Technology, ScaDS.AI Dresden
1 Einleitung
Die Integration Künstlicher Intelligenz (KI) in pädagogische Praktiken
kennzeichnet einen Wandel hin zu zunehmend dynamisch anpassbaren
Lernumgebungen, insbesondere durch den Einsatz KI-gestützter Lernassistenten
(Chen et al., 2020). In der Fachliteratur wird hierfür häufig der Begriff ‚Pedagogical
Agents‘ (PAs) verwendet, der Technologien beschreibt, die darauf abzielen, Wissen
zu vermitteln, Konzepte zu erklären, Feedback zu geben und das Lernen umfassend
zu unterstützen (Beege & Schneider, 2023). In dieser Arbeit verwenden wir den
Begriff ‚KI-gestützter Lernassistent‘ oder ‚KI-Avatar‘, um eine spezifische
Teilmenge von PAs zu definieren, die sich durch den Einsatz fortschrittlicher KI-
Technologien auszeichnet und dadurch eine tiefere sowie personalisierte
Unterstützung ermöglichen (Dede et al., 2019; Zawacki-Richter et al., 2019).
Studien zeigen, dass KI-Avatare im Gegensatz zu traditionellen PAs, die primär als
Vermittler von Wissen fungieren (Bouchet et al., 2016; Dever et al., 2022), das
Potenzial besitzen, individuelle Lernprozesse dynamischer und effektiver zu
unterstützen. Sie bieten personalisiertes Feedback und passen sich kontinuierlich
den Bedürfnissen und Fortschritten der Lernenden an (Pinkwart & Beudt, 2020;
Kochmar et al., 2020). Insbesondere die personalisierte und adaptive Unterstützung
durch KI-Avatare soll selbstgesteuerte Lernerfahrungen im Kontext des situierten
Lernens fördern (Lave & Wenger, 1991; Schmohl, 2021; Plass & Pawar, 2020;
Hummel & Donner, 2023; Lee et al., 2022) und die Lernenden über den gesamten
Bildungsverlauf hinweg unterstützen (Sekeroglu et al., 2019; Ninaus & Sailer,
2022). Trotz aller vordergründigen Potenziale ist es im Bildungskontext
entscheidend, den Einsatz von KI kritisch zu hinterfragen, insbesondere hinsichtlich
11
Die vorliegende Studie weist einige Limitationen auf, die sich aus der bewusst
gewählten homogenen Stichprobe ergeben. Diese Wahl diente der Stärkung der
internen Validität, da eine klar umrissene Zielgruppe eine präzisere Untersuchung
der Forschungsfragen ermöglicht. Allerdings schränkt diese Homogenität die
Generalisierbarkeit der Ergebnisse ein, da die erfassten Einstellungen und
Lernstrategien nicht repräsentativ für andere Fachbereiche oder die gesamte
Studierendenschaft sein könnten. Zukünftige Studien sollten größere und diversere
Stichproben berücksichtigen, um fachspezifische Unterschiede zu erfassen und die
Übertragbarkeit der Ergebnisse zu gewährleisten. Vor diesem Hintergrund gewinnt
auch die Frage nach der effektiven Gestaltung personalisierter Lernansätze durch
KI-Avatare an Bedeutung. Eine solche Anpassung erfordert nicht nur die fundierte
Analyse von Algorithmen, die eine dynamische Berücksichtigung individueller
Lernvoraussetzungen ermöglichen, sondern auch die Integration didaktischer
Prinzipien. Dabei geht es um die Identifikation geeigneter Modelle, wie
personalisierte Empfehlungssysteme oder adaptive Regelungstechniken, sowie um
eine interdisziplinäre Herangehensweise, die Erkenntnisse aus der
Bildungsforschung, Informatik und kognitiven Psychologie verbindet. Nur durch
eine solche mehrdimensionale Perspektive lässt sich sicherstellen, dass die KI-
gestützte Anpassung über algorithmische Muster hinausgeht und die didaktische
Intention der Lernbegleitung gezielt unterstützt.
5 Literaturangaben
Alharbi, S., & Drew, S. (2014). Using the Technology Acceptance Model in
Understanding Academics’ Behavioural Intention to Use Learning
Management Systems. International Journal of Advanced Computer Science
and Applications (IJACSA), 5(1), 143-155.
http://dx.doi.org/10.14569/IJACSA.2014.050120
Baha, T. A., Hajji, M. E., Es-Saady, Y., & Fadili, H. (2023). The impact of
educational chatbot on student learning experience. Education and
Information Technologies, 29, 10153-10176.
https://doi.org/10.1007/s10639-023-12166-w
12
Bartok, L., Donner, M.-T., Ebner, M., Gosch, N., Handle-Pfeiffer, D., Hummel, S.,
Kriegler-Kastelic, G., Leitner, P., Tang, T., Veljanova, H., Winter, C., &
Zwiauer, C. (2023). Learning Analytics – Studierende im Fokus. Zeitschrift
für Hochschulentwicklung: ZFHE; Beiträge zu Studium, Wissenschaft und
Beruf, 18 (Sonderheft Hochschullehre), 223-250.
https://doi.org/10.3217/zfhe-SH-HL/12
Beege, M., & Schneider, S. (2023). Emotional design of pedagogical agents: the
influence of enthusiasm and model‑observer similarity. Education Tech
Research Dev, 71, 859-880. https://doi.org/10.1007/s11423-023-10213-4
Bohlinger, S., & Hummel, S. (2024). Digital Capacity Building in Teacher
Education: An Environmental Case Study from Cambodia. In S. Hummel
(Ed.), Empowering Education in Cambodia and Sri Lanka: Advancing
Quality in 21st Century Teaching and Learning (pp. 31-50). Springer.
https://doi.org/10.1007/978-3-658-43306-2_3
Bouchet, F., Harley, J. M., & Azevedo, R. (2016). Can Adaptive Pedagogical
Agents’ Prompting Strategies Improve Students’ Learning and Self-
Regulation? In A. Micarelli, J. Stamper & K. Panourgia (Eds.), Intelligent
Tutoring Systems. ITS 2016. Lecture Notes in Computer Science (Vol. 9684)
(pp. 368-374). Springer. https://doi.org/10.1007/978-3-319-39583-8_43
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., & Sun, L. (2023). A
Comprehensive Survey of AI-Generated Content (AIGC): A History of
Generative AI from GAN to ChatGPT. ArXiv, abs/2303.04226.
https://doi.org/10.48550/arXiv.2303.04226
Chen, L, Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review.
IEEE Access, 8, 75264-75278.
https://doi.org/10.1109/ACCESS.2020.2988510
Davis, F. D. (1986). A technology acceptance model for empirically testing new
end-user information systems: Theory and results. Massachusetts Institute of
Technology.
Dede, C., Richards, J., & Saxberg, B. (2019). Learning Engineering for Online
Education. Theoretical Contexts and Design-Based Examples. Routledge.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design
elements to gamefulness: defining "gamification". Proceedings of the 15th
International Academic MindTrek Conference: Envisioning Future Media
Environments (MindTrek '11). Association for Computing Machinery, New
York, USA, 9-15. https://doi.org/10.1145/2181037.2181040
Dever, D. A., Sonnenfeld, N. A., Wiedbusch, M. D., & Azevedo, R. (2022).
Pedagogical Agent Support and Its Relationship to Learners’ Self-regulated
Learning Strategy Use with an Intelligent Tutoring System. In M. M.
Rodrigo, N. Matsuda, A. I. Cristea & V. Dimitrova (Eds.), Artificial
Intelligence in Education. AIED 2022. Lecture Notes in Computer Science
13
(Vol. 13355) (pp. 332-343). Springer. https://doi.org/10.1007/978-3-031-
11644-5_27
Egger, R., & Hummel, S. (2020). Stolperstein oder Kompetenzstufe. Die
Studieneingangsphase und ihre Bedeutung für die Wissenschaftssozialisation
von Studierenden. Springer VS. https://doi.org/10.1007/978-3-658-23283-2
Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School Engagement.
In K. A. Moore & L. H. Lippman (Eds.), What do children need to flourish:
Conceptualizing and measuring indicators of positive development (pp. 305–
321). Springer Science. https://doi.org/10.1007/0-387-23823-9_19
Gibson, D. C., & Ifenthaler, D. (2024). AI Roles for Enhancing Individual Learning.
In: Computational Learning Theories. In D. C. Gibson & D. Ifenthaler (Eds.)
Advances in Analytics for Learning and Teaching (pp. 79-92). Springer.
https://doi.org/10.1007/978-3-031-65898-3_7
Granic, A. (2022). Technology Acceptance and Adoption in Education. In O.
Zawacki-Richter & I. Jung (Eds.), Handbook of Open, Distance and Digital
Education (pp. 1-15). Springer. https://doi.org/10.1007/978-981-19-0351-
9_11-1
Griesbeck, A., Zrenner, J., Moreira, A., & Au-Yong-Oliveira, M. (2024). AI in
Higher Education: Assessing Acceptance, Learning Enhancement, and
Ethical Considerations Among University Students. In A. Rocha, H. Adeli,
G. Dzemyda, F. Moreira & A. Poniszewska-Marańda, A. (Eds.), Good
Practices and New Perspectives in Information Systems and Technologies.
WorldCIST 2024. Lecture Notes in Networks and Systems (Vol. 987) (pp.
214-227). Springer. https://doi.org/10.1007/978-3-031-60221-4_21
Güldal, H., & Dinçer, E. O. (2024). Can rule-based educational chatbots be an
acceptable alternative for students in higher education? Education and
Information Technologies. https://doi.org/10.1007/s10639-024-12977-5
Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does Gamification Work? -- A
Literature Review of Empirical Studies on Gamification. 47th Hawaii
International Conference on System Sciences, Waikoloa, USA, 3025-3034.
https://doi.org/10.1109/HICSS.2014.377
Horan, S. M., Martin, M. M., & Weber, K. (2012). Understanding emotional
response theory: The role of instructor power and justice messages.
Communication Quarterly, 60, 210-233.
http://dx.doi.org/10.1080/01463373.2012.669323
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2022). Effects of artificial
Intelligence–Enabled personalized recommendations on learners’ learning
engagement, motivation, and outcomes in a flipped classroom. Computers &
Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
Hummel, S., Donner, M.-T., & Egger, R. (2023). Lehren mit Learning Analytics.
Qualifizierungsprogramm für die Hochschullehre. Universität Graz.
14
https://oer-portal.uni-graz.at/edu-
sharing/components/collections?viewType=1&id=df2fc7c7-b832-421b-
acec-764181c6c2f0
Hummel, S. (2020). Grundlagen der Hochschullehre. Springer.
https://doi.org/10.1007/978-3-658-28181-6
Hummel, S., Egger, R., & Donner, M.-T. (2023). Lehren mit KI: Learning Analytics
für mehr Studierendenorientierung in der Hochschullehre? In T. Köhler, E.
Schopp, N. Kahnwald & R. Sonntag (Eds.), Communities in New Media.
Inclusive digital: Forming Community in an Open Way Self-determined
Participation in the Digital Transformation: Proceedings of 26th Conference
GeNeMe (pp. 7-16). TUDpress. https://doi.org/10.25368/2024.7
Hummel, S., & Donner, M.-T. (2023). KI-Anwendungen in der Hochschulbildung
aus Studierendenperspektive. FNMA-Magazin, 3, 38-41.
https://doi.org/10.13140/RG.2.2.13967.97446
Hummel, S., & Donner, M.-T. (2024). Digital Companions for Learners: Design
Principles for AI Assistants in Education. The Paris Conference on
Education (PCE2024; 13.-17.06.2024).
Hummel, S., Aldrian, S., & Sheehan, B. (2024). Culture, Education and Digital
Transformation in Sri Lanka. In S. Hummel (Ed.), Empowering Education in
Cambodia and Sri Lanka: Advancing Quality in 21st Century Teaching and
Learning (pp. 9-29). Springer. https://doi.org/10.1007/978-3-658-43306-2_2
Hummel, S., Brodacz-Geier, M., & Sheehan, B. (2024). Pedagogical
Professionalization in Digital Contexts. In S. Hummel (Ed.), Empowering
Education in Cambodia and Sri Lanka: Advancing Quality in 21st Century
Teaching and Learning (pp. 203-219). Springer. https://doi.org/10.1007/978-
3-658-43306-2_13
Hummel, S., Bohlinger, S., & Sheehan, B. (2024). Modern Pedagogies in the Digital
Era: Elevating Educational Standards in Teacher Education. In S. Hummel
(Ed.), Empowering Education in Cambodia and Sri Lanka: Advancing
Quality in 21st Century Teaching and Learning (pp. 65-76). Springer.
https://doi.org/10.1007/978-3-658-43306-2_5
Hummel, S. (2024, in press). Learning with AI – a Surface Phenomenon? Heliyon.
Kim & Baylor (2015). Research-Based Design of Pedagogical Agent Roles: a
Review, Progress, and Recommendations. International Artificial
Intelligence in Education Society, 26, 160-169.
https://doi.org/10.1007/s40593-015-0055-y
Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020).
Automated Personalized Feedback Improves Learning Gains in An
Ingelligent Tutoring System. In I. Bittencourt, M. Cukurova, K. Muldner, R.
Luckin & E. Millán (Eds.), Artificial Intelligence in Education. AIED 2020
(pp. 140-146). Lecture Notes in Computer Science (Vol. 12164). Springer.
15
https://doi.org/10.1007/978-3-030-52240-7_26
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral
participation. Cambridge University Press.
https://doi.org/10.1017/CBO9780511815355
Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based chatbot on
college students’ after-class review, academic performance, self-efficacy,
learning attitude, and motivation. Educational Technology Research &
Development, 70(5), 1843-1865. https://doi.org/10.1007/s11423-022-10142-
8
Ninaus, M., & Sailer, M. (2022). Zwischen Mensch und Maschine: Künstliche
Intelligenz zur Förderung von Lernprozessen. Lernen und Lernstörungen,
11(4), 213-224. https://doi.org/10.1024/2235-0977/a000386
Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: an
emerging pedagogical approach enabled by a smart learning environment.
Smart Learning Environments, 6, 9. https://doi.org/10.1186/s40561-019-
0089-y
Pinkwart, N., & Beudt, S. (2020). Künstliche Intelligenz als unterstützende
Lerntechnologie. Frauenhofer. https://publica-
rest.fraunhofer.de/server/api/core/bitstreams/2d4f9603-b95c-4548-912a-
d4a3b596ebf1/content
Pintrich, P. R., & de Groot, E. V. (1990). Motivational and self-regulated learning
components of classroom academic performance. Journal of Educational
Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
Plass, J. L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning.
Journal of Research on Technology in Education, 52(3), 275-300.
https://doi.org/10.1080/15391523.2020.1719943
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and Extrinsic Motivations: Classic
Definitions and New Directions. Contemporary Educational Psychology,
25(1), 54-67. https://doi.org/10.1006/ceps.1999.1020
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model
(TAM): A meta-analytic structural equation modeling approach to explaining
teachers’ adoption of digital technology in education. Computers &
Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Schmohl, T. (2021). Situiertes Lernen. In T. Schmohl & T. Philipp (Eds.),
Handbuch Transdisziplinäre Didaktik (pp. 301-312). Transcript.
https://doi.org/10.1515/9783839455654-028
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). Artificial Intelligence in
Education: Application in Student Performance Evaluation. Dilemas
Contemporáneos: Educatión, Política y Valores, 7(1), 1-21.
Siegle, R. F., Schroeder, N. L., Lane, H. C., & Craig, S. D. (2023). Twenty‑five
Years of Learning with Pedagogical Agents: History, Barriers, and
16
Opportunities. TechTrends, 67, 851-864. https://doi.org/10.1007/s11528-
023-00869-3
Stützer, C. M., & Herbst, S. (2021). KI-Akzeptanz in der Hochschulbildung. Zur
Operationalisierung von Einflussfaktoren auf die Akzeptanz intelligenter
Bildungstechnologien. In H.-W. Wollersheim, M. Karapanos & N. Pengel
(Hrsg.), Bildung in der digitalen Transformation (S. 293-302). Waxmann.
https://doi.org/10.25656/01:26645
Taylor, D.L., Yeung, M., & Bashet, A.Z. (2021). Personalized and Adaptive
Learning. In J. Ryoo & K. Winkelmann (Eds), Innovative Learning
Environments in STEM Higher Education (pp. 17-34). Springer.
https://doi.org/10.1007/978-3-030-58948-6_2
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology
acceptance model: four longitudinal field studies. Management Science, 46,
186-204.
Wang, S., & Sun, Z. (2024). Roles of artificial intelligence experience, information
redundancy, and familiarity in shaping active learning: Insights from
intelligent personal assistants. Education and Information Technologies.
https://doi.org/10.1007/s10639-024-12895-6
Watanabe, A., Schmohl, T., & Schelling, K. (2023). Akzeptanzforschung zum
Einsatz Künstlicher Intelligenz in der Hochschulbildung. Eine kritische
Bestandsaufnahme. In C. de Witt, C. Gloerfeld & S. E. Wrede (Hrsg.),
Künstliche Intelligenz in der Bildung (S. 263-290). Springer Fachmedien
Wiesbaden. https://doi.org/10.1007/978-3-658-40079-8_13
Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Systematic
review of research on artificial intelligence applications in higher education
– where are the educators? International Journal of Educational Technology
in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X., Chu, X., Chai, C. S., Yung Jong, M. S., Istenic, A., Spector, M., Liu, J.-
B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in
Education from 2010 to 2020. Complexity, Vol. 2021, 1-18. h
https://doi.org/10.1155/2021/8812542
Zimmerman, B. J. (1989). A Social Cognitive View of Self-Regulated Academic
Learning. Journal of Educational Psychology, 81(3), 329-339.
https://doi.org/10.1037/0022-0663.81.3.329
ResearchGate has not been able to resolve any citations for this publication.
Can rule-based educational chatbots be an acceptable alternative for students in higher education?
Article
Full-text available
  • Aug 2024
  • Educ Inform Tech
The purpose of this study was to investigate student perceptions and acceptance of a rule-based educational chatbot in higher education, employing the TAM (Technology Acceptance Model) framework. The researchers developed a rule-based chatbot for this purpose and examined the students' technology acceptance using qualitative research methods. Therefore, the study was design-based research using qualitative research methods. The participants of the study comprised 22 students studying in the Science Teaching program of Trakya University Faculty of Education and enrolled in the Modern Physics Course in the 2021–2022 fall semester. The research revealed that students' technology acceptance towards rule-based chatbots was high, even though these chatbots had technological limitations when compared to machine learning or deep learning-based ones. The students found rule-based chatbots to be useful, especially in terms of response quality, information quality, and access. Additionally, some technical details and open-source codes were also presented in the study, which can be a guide for rule-based chatbots to be designed for other areas of education.
View
Roles of artificial intelligence experience, information redundancy, and familiarity in shaping active learning: Insights from intelligent personal assistants
Article
Full-text available
  • Jul 2024
  • Educ Inform Tech
Artificial Intelligence (AI) is increasingly being integrated into educational settings, with Intelligent Personal Assistants (IPAs) playing a significant role. However, the psychological impact of these AI assistants on fostering active learning behaviors needs to be better understood. This research study addresses this gap by proposing a theoretical model to outline and predict active learning dynamics. Data was collected from 237 validated questionnaires and analyzed using partial least squares structural equation modeling. Our results confirm most hypotheses advanced in our model, and information redundancy has an unexpected negative and indirect influence on active learning, while perceived familiarity and system quality are positive drivers. Crucial mediators such as perceived usefulness, ease of use, and convenience significantly positively influence active learning outcomes. Interestingly, the relationship between perceived ease of use, perceived convenience, and active learning is positively moderated by AI experience. The most striking and unexpected finding of this study is the preference of university students for familiar systems over high-tech learning methods. This result challenges the common belief that the younger generation is always eager to adopt the latest technology. Instead, our findings suggest that students value convenience and familiarity over novelty in learning systems. This preference is reflected in their systematic evaluation, where convenience and familiarity are considered top priorities. This study provides valuable insights into the potential of AI to enrich the learning experience, thus making it especially relevant to professionals interested in artificial intelligence in international business education.
View
Akzeptanzforschung zum Einsatz Künstlicher Intelligenz in der Hochschulbildung. Eine kritische Bestandsaufnahme
Chapter
Full-text available
  • Nov 2023
Innovationen in der Hochschulbildung führen nicht per se zu gesteigerter Nachfrage oder Akzeptanz aufseiten der relevanten Bildungsgruppen (Krone & Pinkl 2017). Im Fall KI-ge-stützter Innovationen im Kontext akademischen Lehrens und Lernens kommt erschwerendhinzu, dass weder Lernende noch Lehrende der Technik unvoreingenommen begegnen: Vorbehalte gegenüber KI werden hier zu kritischen Erfolgsfaktoren für die Implementierung in-novativer Technologie (Schäfer & Keppler 2013, S. 5). Während im technologischen, bildungswissenschaftlichen und didaktischen Bereich bereits Forschungsergebnisse und auch erste Implementationen vorliegen, ist die Akzeptanzforschung vor allem in Bezug auf die Studierendenperspektive gegenüber KI-Anwendungen noch vergleichsweise gering entwickelt (Kieslich et al. 2019). Der vorliegende Beitrag adressiert diesen »blinden Fleck« anhandeiner Übersicht über grundlegende Konzepte und Modelle aus dem Bereich der Akzeptanz-forschung. Anhand aktueller KI-Trends werden zunächst allgemeine Überlegungen zur Ak-zeptanz abgeleitet. Auf Basis theoretischer Grundlagen zum Gegenstand der Akzeptanz, der Exemplifikation an einem Fallbeispiel sowie einer Synopse relevanter Modelle wird an-schließend kritisch diskutiert, welche Bedeutung der Akzeptanzforschung zum KI-Einsatz in der Hochschulbildung künftig zugemessen werden kann. Der Beitrag stellt eine Anregung und Orientierung für empirische Untersuchungen dar.
View
Culture, Education and Digital Transformation in Sri Lanka
Chapter
Full-text available
  • Dec 2023
This article provides a multifaceted examination of Sri Lanka's rich socio-cultural history, current socio-economic challenges, and the pivotal role of education, particularly focusing on teacher education. The text begins by exploring Sri Lanka's intricate history and cultural diversity, reflecting the influences from various ethnic communities and colonial rule. It then navigates through the country's recent developments, including the brutal civil war, economic crisis, and COVID-19 pandemic. In the educational landscape, the article delves into the structure and content of Sri Lanka's educational system, highlighting the commitment to universal and free education, a 93 % literacy rate, and the diverse types of schools, but also pointing out disparities between urban and rural areas and gender biases. Special emphasis is placed on the structure, challenges, and potential reforms within teacher education. This includes detailed insights into pre-service and in-service teacher training, the unique challenges in peripheral areas, the need for innovative pedagogical strategies, and the role of various governmental and educational entities. The article concludes by synthesizing the insights into a call for a comprehensive, context-specific, and adaptive approach towards teacher education, aiming to create a robust and equitable system aligned with the needs of 21st-century learners. The analysis integrates historical, cultural, educational, and economic perspectives to provide a cohesive understanding of Sri Lanka's complex educational landscape and offers targeted recommendations for transformative change.
View
Modern Pedagogies in the Digital Era: Elevating Educational Standards in Teacher Education
Chapter
Full-text available
  • Dec 2023
This article discusses the role of the EU-funded project ‘CONTESSA – Contemporary teaching skills’ in modernizing teacher training methodologies in the two Asian countries Cambodia and Sri Lanka. Focusing on the pressing need to adapt to 21st-century teaching and learning paradigms, CONTESSA's design reflects an understanding of the technological disparities across these countries. While acknowledging the resource constraints, the program centers its e-learning curriculum on empowering teachers to enhance lesson planning and teaching quality. The program introduces multiple modules, each targeting specific pedagogical skills ranging from digital teaching, learner-centric approaches, embracing classroom diversity, to understanding gender-based nuances in education. Additionally, the article underscores the significance of training teacher trainers to ensure the program's sustainability, detailing a comprehensive Train-the-Trainer initiative that encompasses face-to-face workshops, tutorials, and a user manual. Derived from a four-year-long project, findings indicate the potential of the CONTESSA program in fostering a shift towards widely accessible teacher training, aiming to equip educators with crucial 21st-century teaching skills and promote holistic student learning. The program's broader vision emphasizes a collaborative community of practice, ensuring the professional growth of educators and impactful learning experiences for students.
View
Pedagogical Professionalization in Digital Contexts
Chapter
Full-text available
  • Dec 2023
The promotion of pedagogical professionalization is central for quality teaching and for the development of a teacher’s professional identity. This paper deals with the question of how a comprehensive pedagogical training program for teacher education supporting 21st century teaching and learning in Cambodian and Sri Lankan primary schools can be designed, including the pedagogically effective use of digital resources. For this purpose, pedagogical professionalization is first examined theoretically and related to the requirement structures of teacher education. Subsequently, the role of digital technologies in pedagogical professionalization processes is discussed. The core of this article consists of the presentation of the online course developed in the EU co-funded Capacity Building in Higher Education project CONTESSA – Contemporary Teaching Skills for South Asia. This open educational resource (OER) course offers a pedagogical teacher education program based on learner-centered, constructivist perspectives following Dewey, Kember and Hoidn. In addition to theoretical approaches, the CONTESSA modules include a multifaceted methodological repertoire that enables a theory-based, practice-oriented and (self-)reflective examination within the framework of pedagogical professionalization processes.
View
AI Roles for Enhancing Individual Learning
Chapter
  • Jul 2024
This chapter uses the micro-level network model as a prompting framework for the development of AI applications that can intervene in individual learning processes.
View
Digital Companions for Learners: Design Principles for AI Assistants in Education
Poster
  • Jun 2024
AI-based learning assistants are gaining popularity at a rapid pace. This research is dedicated to establishing empirically validated design principles for the effective development and implementation of AI assistants in educational settings. Our primary focus is on ensuring learner acceptance and accommodating their preferences. How can AI assistants be optimally designed to enhance learner acceptance and cater to their preferences in educational environments? Rooted in the KIAM model, this research examines AI acceptance, emphasizing the alignment between technology and learner needs. The methodology includes qualitative and quantitative approaches – surveys and focus groups with learners to assess attitudes, behaviors, and preferences. Experimental studies observe interactions between learners and prototype AI assistants, providing data on usability and effectiveness. Key findings highlight transparency, trust, and fairness as crucial components driving technology acceptance. Based on focus groups (n = 24), the study proposes AI assistant design principles such as user-friendly interfaces, consistent design, adaptability to individual learner needs, customization options, and emotional intelligence of the learning companion. In this context, emotional intelligence entails the AI assistant responding empathetically to students' emotional expressions and offering constructive guidance. Additionally, the importance of integrating gamification for motivation is emphasized. These principles aim to enhance aesthetic appeal, support functionality, and ensure a positive learning experience. A personalized AI assistant considers learning pace, rhythm, and progress tracking for targeted interventions. These empirically grounded design principles offer a comprehensive framework for developers and educators to create AI assistants that align with user needs, promoting acceptance, and fostering positive learning outcomes. AI-based learning assistants are gaining popularity at a rapid pace. This research is dedicated to establishing empirically validated design principles for the effective development and implementation of AI assistants in educational settings. Our primary focus is on ensuring learner acceptance and accommodating their preferences. How can AI assistants be optimally designed to enhance learner acceptance and cater to their preferences in educational environments? Rooted in the KIAM model, this research examines AI acceptance, emphasizing the alignment between technology and learner needs. The methodology includes qualitative and quantitative approaches – surveys and focus groups with learners to assess attitudes, behaviors, and preferences. Experimental studies observe interactions between learners and prototype AI assistants, providing data on usability and effectiveness. Key findings highlight transparency, trust, and fairness as crucial components driving technology acceptance. Based on focus groups (n = 24), the study proposes AI assistant design principles such as user-friendly interfaces, consistent design, adaptability to individual learner needs, customization options, and emotional intelligence of the learning companion. In this context, emotional intelligence entails the AI assistant responding empathetically to students' emotional expressions and offering constructive guidance. Additionally, the importance of integrating gamification for motivation is emphasized. These principles aim to enhance aesthetic appeal, support functionality, and ensure a positive learning experience. A personalized AI assistant considers learning pace, rhythm, and progress tracking for targeted interventions. These empirically grounded design principles offer a comprehensive framework for developers and educators to create AI assistants that align with user needs, promoting acceptance, and fostering positive learning outcomes.
View
AI in Higher Education: Assessing Acceptance, Learning Enhancement, and Ethical Considerations Among University Students
Chapter
  • May 2024
This scientific paper aims to investigate the multifaceted impact of Artificial Intelligence (AI) on students at universities, focusing on the three key areas: Acceptance of AI in Higher Education (HE), Learning Enhancement and Ethical Considerations. A survey with 100 valid answers from Graduates and Undergraduates was conducted, to gather insights into their perceptions and experiences with AI-powered tools. The investigation shows that these tools are widely accepted. There is a significant positive correlation between students’ comfort level with incorporating AI into their education and their familiarity with them. Regarding the improvement of learning, more than half of the participants think that AI technologies are useful for the understanding of course material, and that their learning efficiency has increased. Also, it could be statistically proven that there is a significant positive correlation between frequency of preparation and performance improvement for exams, essays, and projects. Concerning ethics, most of the students are aware of possible ethical dilemmas and agree that standards are necessary. The study also shows that 75% witnessed other students cheating with the help of AI-tools. Non-parametric Mann-Whitney tests were also used and gender was found to significantly affect the variables under study. This investigation serves as a foundation for informed discussions on harnessing the potential of AI to improve education while addressing the associated concerns.
View
View