Background

Special Issue of Journal of the Learning Sciences

Guest Editors:

Megan Humburg, Indiana University Bloomington Haesol Bae, University at Albany, State University of New York Ingo Kollar, Universität Augsburg

As the development of AI technologies for education continues at a rapid pace (Prahani et al., 2022), the field of the Learning Sciences has a vital role to play in the conversation about how AI tools should be designed and implemented to support learning and teaching along multiple dimensions, including but not limited to sociocultural, psychological (cognitive, motivational, and emotional), and ethical layers. Issues of privacy, surveillance, and algorithmic bias present significant challenges to the design and implementation of AI tools in physical and virtual learning spaces (Akgun & Greenhow, 2021). Many students and teachers also tend to view AI systems as a “black box” in terms of how their information is used (or misused) (Greenwald et al., 2021). Research in the Learning Sciences can help us design and implement AI tools that are effective, responsible, and meaningful for analyzing learning and teaching (Luckin & Cukurova, 2019). As researchers in the Learning Sciences have explored the potential of AI, they have revealed important intersections between AI and learning, such as how AI can support ambitious learning practices (Hmelo-Silver et al., 2022), how to integrate AI with equitable design (Walsh et al., 2022), and youth perspectives and critical sensemaking around AI (Morales-Navarro et al., 2023).

The urgent need to thoughtfully integrate learning theories into the design and teaching of AI is further highlighted by the public conversations occurring in governmental, educational, and industry sectors that seek to establish shared norms for how and why AI tools are used (e.g., The White House, 2023; Council of the EU, 2023; UNESCO, 2023; Software & Industry Information Association, 2023; Ruiz et al., 2022). As we try to make sense of how increasingly powerful AI capabilities may shift practices of teaching and learning, scholars and educators alike have been calling for a deeper understanding of how AI impacts the fabric of learning environments, emphasizing the need to move beyond technical discussions to ethical and practical ones (e.g., Akgun & Greenhow, 2021; Kasneci et al., 2023). Issues such as how to center students’ voices in the AI design process (Hasse et al., 2019), address algorithmic bias and discrimination along gendered and racialized lines (Buolamwini & Gebru, 2018), and improve AI literacy for learners and teachers (Wang & Lester, 2023) are just a few of the many areas in which the Learning Sciences can utilize our theories and methods to make sense of the sociocultural, psychological, and ethical layers of how AI impacts teaching and learning.

Aims and Scope

For this special issue, we invite authors who are exploring AI technologies for teaching and learning through a humanizing lens. We are particularly interested in submissions that leverage human- centered design, participatory design, research-practice partnerships, and other approaches that deeply consider how artificial intelligence impacts learners, educators, and their communities in multifaceted ways. Our goal is to gather perspectives from the Learning Sciences that can help to inform researchers, designers, and users of AI tools of the ways in which AI can be leveraging in dignity-affirming and empowering ways.

Studies might analyze learning and teaching with AI from the perspective of sociocultural, psychological, and ethical layers of learning interactions, ideally weaving several of these layers together in their analysis. We are interested in contributions that explore not only what AI technologies can do for teaching and learning, but also how the integration of AI alters what teaching and the process of learning look like, for better or for worse. How do our notions of assessment, curricular design, and even our understanding of what it means to learn shift when learners utilize advanced generative AI as a part of the learning process? What new opportunities do AI technologies offer to learners in terms of agency and identity, and what risks do they pose for students’ dignity and privacy? What boundaries do we (or should we) create when using these tools for educational pursuits? We welcome both empirical and conceptual/theoretical submissions that explore these grey areas and grapple with both the possibilities and the pitfalls of AI technologies for teaching and learning.

Papers in the special issue could address, but are not limited to, the following themes:

• ?  Impact of AI on Learning and Teaching Processes
  • ?  How AI shifts student learning practices, teacher pedagogical practices, and/or definitions of what it means to learn and to teach with AI
  • ?  The impact of AI tools on learners’ emotions, social connections, and community
  • ?  The impact of AI tools on student engagement
  • ?  The skills/competencies learners need to interact productively with generative AI, and how to support these skills
• ?  The Role of a “Human-in-the-Loop” in AI tools for Learning
  • ?  Understanding what tasks and responsibilities are appropriate and effective to off-load onto AI tools for teachers and learners
  • ?  Finding meaningful balance between teacher involvement and AI-based support for specific learning environments
  • ?  Multimodal approaches to AI-driven analysis of learning that integrate human and machine approaches to understanding the learning process, or that bring human-driven and machine-driven analytic approaches into conversation with one another
• ?  Innovative Human-Centered Designs with AI
  • ?  Designs of humanizing curriculum for AI-supported learning
  • ?  Designs for improving AI literacy for students, teachers, and / or families
  • ?  Student, teacher, and family perspectives on AI in learning spaces
  • ?  The co-design of AI tools with students and teachers as core participants
• ?  Ethical, Moral, and Critical Considerations

? Ethical and moral dimensions of AI’s impact on learning and teaching, including but not

limited to algorithmic bias and data privacy

• ?  Critical perspectives on the role of AI in learning and teaching
• ?  Evaluation of AI tools and identifying risks
• ?  AI for advancing inclusion, diversity, equity, and accessibility (IDEA) in education Papers should draw on theories and methods from the Learning Sciences, but integrations/collaborations across multiple disciplines and academic fields are welcome and encouraged. Studies may also explore a wide variety of content domains. In their submissions, we encourage authors to consider questions about who AI tools are designed for, why they are designed, and for what ends. We are interested in studies that richly explore processes of learning and teaching that are supported by AI tools or involved in building AI literacies and are looking particularly for papers that center the learning experiences (rather than centering the technology). In other words, education supported by AI or for learning about AI, rather than AI for education. Learning environments can be conceptualized broadly, and papers may address in- and/or out-of-school contexts, spanning K-12, higher education, and/or adult learning. Papers may also explore a variety of AI-driven technologies, including but not limited to generative AI, large language models, and multimodal learning analytics. Plan for soliciting and organizing articles in the special issue We invite interested authors to submit 1000-word extended abstracts that describe the key ways in which their paper will explore how we as educational researchers and designers can humanize the designs of AI technologies and teaching with AI concepts, centering the needs of learners, teachers, families, and/or communities in our decisions about where, how, and why we integrate AI into learning environments. The guest editors will select from the pool of abstracts and invite selected authors to submit full manuscripts of no more than 10,000 words and follow JLS submission guidelines. All full manuscripts will undergo a rigorous peer-review process and will receive detailed feedback from the editors, and if accepted, manuscripts will be published according to the timeline below. Abstracts should be submitted to the Qualtrics form here by June 30th, 2024. Feel free to contact the guest co-editors with any questions: Megan Humburg (mahumbur@indiana.edu) Haesol Bae (hbae4@albany.edu)
  Ingo Kollar (ingo.kollar@phil.uni-augsburg.de) Timeline ? Paper proposals (1000-word abstracts) due June 30th, 2024
  ? Decisions on proposals/invitations to contribute full papers July 31st, 2024 ? Full manuscripts due December 31st, 2024
  ? The first round of reviews completed February 28th, 2025
  ? First round revisions resubmitted May 30th, 2025
  ? The second round of reviews completed July 31st, 2025
  ? Second round revisions resubmitted October 1st, 2025
  ? Requests for minor revisions & final acceptances sent November 1st, 2025 ? Final versions of all papers due December 1st, 2025
  ? Special issue published in Spring 2026

References

Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2, 431-440. https://doi.org/10.1007/s43681-021-00096-7

Buolamwini, J., & Gebru, T. (2018, January). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77-91). PMLR.

Council of the European Union (2023, December 9). Artificial intelligence act: Council and Parliament strike a deal on the first rules for AI in the world. [Press release]. https://www.consilium.europa.eu/en/press/press-releases/2023/12/09/artificial-intelligence-act- council-and-parliament-strike-a-deal-on-the-first-worldwide-rules-for-ai/

Doroudi, S. (2023) What happened to the interdisciplinary study of learning in humans and machines?, Journal of the Learning Sciences, DOI: 10.1080/10508406.2023.2260159

Greenwald, E., Leitner, M., & Wang, N. (2021, May). Learning artificial intelligence: insights into how youth encounter and build understanding of AI concepts. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 15526-15533).

Hasse, A., Cortesi, S., Lombana-Bermudez, A., & Gasser, U. (2019). Youth and artificial intelligence: Where we stand. Berkman Klein Center Research Publication.

Hmelo-Silver, C. E., Puntambekar, S., Glazewski, K. D., Lawrence, L., Rummel, N., Aleven, V., Biswas, G., Uttamchandani, S., Saleh, A., Bae, H., Brush, T., Mott, B., Lester, J., Goss, W., Gnesdilow, D., Passonneau, R., Singh, P., Kim, C., & Worsley, M. (2022). Artificial intelligence and ambitious learning practices. In Weinberger, A. Chen, W., Hernández-Leo, D., & Chen, B. (Eds.), Proceedings of the 15th International Conference on Computer-Supported Collaborative Learning – CSCL 2022 (pp. 525-532). International Society of the Learning Sciences.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274.

Morales-Navarro, L., Kafai, Y. B., Castro, F., Payne, W., DesPortes, K., DiPaola, D., Williams, R., Ali, S., Breazeal, C., Lee, C., Soep, E., Long, D., Magerko, B., Solyst, J., Ogan, A., Tatar, C., Jiang, S., Chao, J., Rosé, C. P., & Vakil, S. (2023). Making sense of machine learning: Integrating youth’s conceptual, creative, and critical understandings of AI. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences – ICLS 2023 (pp. 1640-1649). International Society of the Learning Sciences.

Prahani, B. K., Rizki, I. A., Jatmiko, B., Suprapto, N., & Amelia, T. (2022). Artificial intelligence in education research during the last ten years: A review and bibliometric study. International Journal of Emerging Technologies in Learning, 17(8).

Luckin, R. & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning

sciences-driven approach. British Journal of Educational Technology, 50: 2824-2838.

https://doi.org/10.1111/bjet.12861

Ruiz, P., Richard, E., Chillmon, C., Shah, Z., Kurth, A., Fekete, A., Glazer, K., Pattenhouse, M., Fusco, J., Fennelly-Atkinson, R., Lin, L., Arriola, S., Lockett, D., Crawford-Meyer, V., Karim, S., Hampton, S., & Beckford, B. (2022). Emerging technology adoption framework: For PK-12 education. [Educator CIRCLS white paper]. Digital Promise. https://doi.org/10.51388/20.500.12265/161

Software & Industry Information Association (2023, October 24). The education technology industry’s principles for the future of AI in Education. https://edtechprinciples.com/principles-for-ai-in- education/

The White House (2023). Blueprint for an AI Bill of Rights: Making automated systems work for the American people. Office of Science and Technology Policy. https://www.whitehouse.gov/ostp/ai- bill-of-rights/

UNESCO (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Walsh, B., Dalton, B., Forsyth, S., Haberl, E., Smilack, J., Yeh, T., Zhang, H., Lee, I., Lin, G. C., Kim, Y. J., Stump, G. S., Stoiber, A., Altuwaiyan, A., Abelson, H., Klopfer, E., Breazeal, C., Wilson, E., Aliabadi, R., Tian, J., Carter, J., Long, D., Magerko, B., & Sengupta-Irving, T. (2022). Aspiring for equity: Perspectives from design of AI education. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences – ICLS 2022 (pp. 1771-1778). International Society of the Learning Sciences.

Wang, N., & Lester, J. (2023). K-12 education in the age of AI: A call to action for K-12 AI literacy. International Journal of Artificial Intelligence in Education, 33, 228–232. https://doi.org/10.1007/s40593-023-00358-x