Announcements

[Call for Papers] Special Issue: „Artificial Intelligence and Education – Empirical and Theoretical Perspectives on Learning“

2026-06-03

Call for papers for a special issue in the journal Breaking Barriers to Learning and Education

https://breaking-barriers.ub.uni-muenchen.de/breakingbarriers/index

Specialized topic: „Artificial Intelligence and Education – Empirical and Theoretical Perspectives on Learning“

Edited by Verena Letzel-Alt and Traugott Böttinger (University of Education Freiburg, Germany)

The journal Breaking Barriers to Learning and Education invites submissions for a special issue on the specialized topic of “Artificial Intelligence (AI) and Education.” This special issue focuses on exploring how artificial intelligence can support teaching and learning processes across diverse educational settings and contribute to the provision of high-quality instruction.

Background and Objectives

Artificial intelligence (AI) is fundamentally transforming educational processes across all levels of education. Adaptive learning systems, AI-assisted diagnostic tools, personalized support services, and AI-driven assistive technologies, among other developments, are creating new opportunities for individualized learning support (Lübken & Wiemer, 2025). In particular, the field of learning is witnessing the emergence of innovative approaches to diagnostics, educational support, and participation (Böttinger & Schulz, 2023; Letzel-Alt & Groß, 2025; OECD, 2025; Schindler et al., 2025).

At the same time, AI-based systems raise fundamental questions concerning education and educational quality (de Witt et al., 2023; particularly Heßdörfer & Moser, 2023, and Wollersheim, 2023; Schiefner-Rohs et al., 2024), instructional design and planning (Letzel-Alt & Groß, 2025; Sponholz & Wolf, 2025), inclusion (Bosse et al., 2019; Böttinger & Tully, 2025), equality of educational opportunity (Autenrieth et al., 2025), the professionalization of educational practitioners (Drolshagen & Haage, 2023; Mertens et al., 2023), data protection and privacy (Scheiter et al., 2025), ethics (Lübken & Wiemer, 2025), as well as the role of social relationship-building within learning processes (Oh & Ahn, 2024). Of particular importance in this context is a multi-perspectival approach that incorporates the diverse stakeholders involved in educational processes, including teachers, students, school administrators, policymakers, parents, and professionals from various disciplines.

This Special Issue seeks to bring together current empirical, theoretical, and conceptual contributions that critically examine the dynamic interplay between technological innovation and pedagogical responsibility. Practice-oriented contributions that illustrate the potential for the high-quality implementation of artificial intelligence in school-based learning contexts are equally welcome.

Potential Topics of Interest

1. AI-Supported Assessment and Diagnostics

  • Adaptive diagnostic approaches in learning and educational contexts
  • Digital formative and summative assessment of learning progress
  • Data-driven planning and implementation of support measures
  • AI-assisted feedback and evaluation systems

2. Personalized Learning Environments and Instructional Design

  • Intelligent tutoring systems and adaptive learning platforms in (inclusive) educational settings
  • AI-supported differentiation and individualized instruction
  • Digital support for diverse learning processes and learner needs
  • Gamification and AI-enhanced motivational strategies
  • Development, implementation, and evaluation of AI-supported learning materials

3. Participation and Inclusion through AI

  • AI as an assistive technology within school-based learning processes
  • Accessible learning environments and AI-supported pathways to participation
  • Promotion of self-regulated learning and learner motivation through AI-based systems

4. Professionalization and Teacher Education

  • AI-related competencies in teacher education, particularly in special and inclusive education
  • Transformations in professional roles and educational practices
  • Human–AI collaboration in classroom contexts
  • Critical reflection on the pedagogical use of AI
  • Evolving forms of assessment in teacher education and classroom instruction
  • Professional development needs, models, implementation strategies, and effectiveness

5. Ethical, Legal, and Societal Dimensions

  • Data protection, privacy, and data sensitivity concerning vulnerable learner populations
  • Bias, discrimination, and fairness in algorithmic systems
  • Educational equity, inclusion, and the digital divide
  • Normative and ethical perspectives on AI in inclusive educational systems

6. Theoretical and Interdisciplinary Perspectives

  • Educational and pedagogical theories of AI in school-based learning contexts
  • Special and inclusive education theories in the context of digital transformation
  • Interdisciplinary approaches integrating perspectives from computer science, educational science, psychology, disability studies, and related fields

Basically, the contributions should allign with the general aims and scope of the journal: https://breaking-barriers.ub.uni-muenchen.de/breakingbarriers/about

Types of Contributions

The Special Issue welcomes a broad range of scholarly contributions, including:

  • Empirical research articles employing qualitative, quantitative, or mixed-methods approaches
  • Theoretical and conceptual papers
  • Systematic literature reviews and meta-analyses
  • Scientifically grounded implementation and practice reports
  • Discussion papers and critical reflective essays

Submission Guidelines

  • Contributions may be submitted in either German or English.
  • Authors are invited to submit an abstract of the proposed contribution not exceeding 500 words (excluding references). The submission should also include a brief summary, a set of keywords, and a statement indicating the contribution’s relevance to one of the thematic areas of the Special Issue.
  • Abstract Submissions must be made via email: verena.letzel-alt@ph-freiburg.de
  • Following the review of submitted abstracts, authors will receive feedback and, where appropriate, an invitation to submit a full manuscript (approximately 40,000 characters, excluding references) by March 1, 2027.
  • Full manuscript submissions must be made via the online platform of the journal Breaking Barriers to Learning and Education: https://breaking-barriers.ub.uni-muenchen.de/breakingbarriers
  • All submissions will undergo a double-blind peer-review process.
  • Authors are requested to consult the journal’s formatting guidelines and author instructions available on the journal website prior to submission.

 

Preliminary Timeline

  • Abstract Submission: August 31, 2026
  • Notification of Abstract Acceptance: September 15, 2026
  • Full Paper Submission: 01.11.2026 - 01.03.27
  • Publication of Papers: Contributions will be published individually following review and formatting

 

For inquiries, please contact the editors of the special issue at: verena.letzel-alt@ph-freiburg.de

 

 

References

Autenrieth, D., Schluchter, J.-R. & Schulz, L. (2025). AI is all you need? Künstliche Intelligenz, gesellschaftliche Teilhabe und Perspektiven transformativer Bildung auf die Herausforderungen eines AI divide [AI is all you need? Artificial Intelligence, Social Participation, and Perspectives on Transformative Education in the Face of the Challenges of an AI Divide]. In Zeitschrift für Inklusion, 3-2025, https://www.inklusion-online.net/index.php/inklusion-online/article/view/840

Bosse, I., Schluchter, R.-J. und Zorn, I. (Hrsg.) (2019). Handbuch Inklusion und Medienbildung [Handbook of Inclusion and Media Education]. 1. Auflage. Weinheim: Beltz Juventa.

Böttinger, T. und Schulz, L. (2023): Teilhabe an digital-inklusiven Bildungsprozessen. Das Universal Design for Learning diklusiv als methodisch-didaktischer Unterrichtsrahmen [Participation in Digitally Inclusive Educational Processes: Universal Design for Learning (diklusiv) as a Methodological and Didactic Instructional Framework]. QfI - Qualifizierung für Inklusion (5)2. https://doi.org/10.25656/01:30168

Böttinger, T. & Tully, J. (2025). Mit KI Barrieren abbauen - KI als vielfältige Lernunterstützung im inklusiven Unterricht [Breaking Down Barriers with AI – AI as Diverse Learning Support in Inclusive Education]. In H. Lichtenstern (Hrsg.). Praxisratgeber: Künstliche Intelligenz als Lernhelfer. Wie KI-Tools das Lernen unterstützen (S.26-28). Friedrich Verlag.

De Witt, C., Gloerfeld, C. und Wrede, S. E. (2023). Künstliche Intelligenz in der Bildung [Artificial Intelligence in Education]. Wiesbaden: Springer VS. https://link.springer.com/content/pdf/10.1007/978-3-658-40079-8.pdf [30.03.2026].

Drolshagen, B. und Haage, A. (2023). Beeinträchtigungsspezifische Medienkompetenz und barrierefreie Lernumgebungen als Voraussetzungen zur Gestaltung passgenauer Übergänge – Konsequenzen für die Lehramtsausbildung [Disability-Specific Media Literacy and Accessible Learning Environments as Prerequisites for Designing Tailored Transitions – Implications for Teacher Education]. In: Joachim Betz und Jan-René Schluchter (Hrsg.). Schulische Medienbildung und Digitalisierung im Kontext von Behinderung und Benachteiligung, S. 334-247. Weinheim: Beltz Juventa.

Heßdörfer, F. & Moser, E. (2023). KI und graue Intelligenz. Bildungstheoretische Perspektiven auf Lerntechnologie und ihre Akteure [AI and Gray Intelligence: Educational-Theoretical Perspectives on Learning Technology and Its Actors]. In: De Witt, C., Gloerfeld, C. und Wrede, S. E. (2023). Künstliche Intelligenz in der Bildung, 47-68. Wiesbaden: Springer VS.

Letzel-Alt, V. und Groß, N. (2025). „Kann Inklusiv!?“ – Status quo des Einsatzes und Potenziale von Künstlicher Intelligenz (KI) zur inklusiven Unterrichtsgestaltung aus Perspektive von Schulleitungen an Sekundarschulen [“Can Inclusive!?” – The Status Quo of the Application and Potential of Artificial Intelligence (AI) for Inclusive Classroom Design from the Perspective of Secondary School Principals]. phpublico, 15, S. 146-156. https://doi.org/10.5281/zenodo.1757709

Lübken, A. und Wiemer, M. (2025). Haltung, Ethik und Verantwortung [Attitude, Ethics, and Responsibility]. In: Künstliche Intelligenz in Bildungseinrichtungen. Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-50188-4_5

Mertens, C., Kamin, A.-M. und Kämper, L.-M. (2023). Digitalisierungsbezogene Kompetenzanforderungen unter der Perspektive von Inklusion – Überlegungen für ein phasenübergreifendes Kompetenzprofil für (angehende) Lehrkräfte [Digitalization-Related Competency Requirements from the Perspective of Inclusion – Considerations for a Cross-Phase Competency Profile for (Prospective) Teachers]. In: Joachim Betz und Jan-René Schluchter (Hrsg.). Schulische Medienbildung und Digitalisierung im Kontext von Behinderung und Benachteiligung, S. 348-368. Weinheim: Beltz Juventa.

OECD (2025). Empowering learners for the age of AI: An AI literacy framework for primary and secondary education. OECD. Paris. https://ailiteracyframework.org

Ohn, S. und Ahn, Y. (2024). Exploring Teachers’ Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education. Human Computer Interaction.  https://doi.org/10.48550/arXiv.2405.13065

Scheiter, K., Bauer, E., Omarchevska, Y., Schumacher, C., und Sailer, M. (2025). Künstliche Intelligenz in der Schule. Eine Handreichung zum Stand in Wissenschaft und Praxis [Artificial intelligence in schools: A guide to the current state of research and practice]. https://www.empirische-bildungsforschung-bmbfsfj.de/img/KI_Review_20250318_Veroeffentlichung.pdf [Zugriff am: 30.03.2026].

Schiefner-Rohs, M., Hofhues, S. & Breiter, A. (2024). Vermessung von Bildung verstehen - Datenbildung ermöglichen [Understanding the Measurement of Education – Enabling Data Literacy]. In M. Schiefner-Rohs, S. Hofhues & A. Breiter (Hrsg.). Datafizierung (in) der Bildung. Kritische Perspektiven auf digitale Vermessung in pädagogischen Kontexten (S.367-384). Transcript. Bielefeld.

Schindler, M., Simon, A.L., Lai, J., Asghari, P., Baumanns, L., Kölsch, M-M. & Lilienthal, A.J. (2025). AI-Based Adaptive Learning Support for the Diagnosis and Promotion of Basic Mathematical Competencies in an Inclusive Context]. In: Katja Beck, Rosa Anna Ferdigg, Dieter Katzenbach, Julia Kett-Hauser, Sophia Laux, Michael Urban (Hrsg.). Förderbezogene Diagnostik in der inklusiven Bildung. Kompetenzbereiche – Fachdidaktik, S. 255-270. Münster, New York: Waxmann. https://doi.org/10.31244/9783830999607

Sponholz, J. & Wolf, K. (2025). Einsatz von künstlicher Intelligenz zur Unterrichtsplanung in inklusiven Kontexten [The Use of Artificial Intelligence for Lesson Planning in Inclusive Contexts]. Televizion, 38/2025, 32-39.

Wollersheim, H.-W. (2023). Bildung durch Künstliche Intelligenz ermöglichen. Ein Beitrag aus bildungstheoretischer Perspektive [Enabling Education through Artificial Intelligence: A Contribution from a Theoretical Perspective on Education]. In: De Witt, C., Gloerfeld, C. und Wrede, S. E. (2023). Künstliche Intelligenz in der Bildung, 3-30. Wiesbaden: Springer VS.