Rowsell, Jennifer;Keune, Anna;Buxton, Alison;Peppler, Kylie
Seeking Languagelessness: Maker Literacies Mindsets to Disrupt Normative Practices
Reading Research Quarterly
2024

Keune, A., Hurtado, S., Simšič, Ž., & Kappes, V.
Practicing AI Ethics Literacy: 10 scenarios for engaging with AI ethics in education
Practicing AI ethics literacy can enhance the ability to make informed decisions about what AI technologies to use in educational settings and how to use them. The 10 AI ethics scenarios presented in this document show examples of how AI technologies could be used in educational contexts. The scenarios are based on selected reports of how AI technologies have been envisioned for educational settings in student-centered ways. The purpose of the scenarios is to foster discussions about AI ethics, including how AI technologies are implemented in educational settings, what they mean for teaching and learning, and what ethical considerations result from that. Through such conversations, AI ethics literacy can develop, which refers to the ability to identify and communicate about ethical implications of implementing AI technologies in educational settings. The scenarios are productive for fostering AI ethics literacy in relation to a diverse set of AI technologies with different functionalities and associated ethical and legal risks for education.
2024

Rook, M. M., Asino, T. I., McDonald, S.P., Keune, A., Kumar, V., Tietjen, P., Tissenbaum, M., Yeoman, P., & Smith, B.
Interrogating and Recoupling Learning and Knowledge with Power and Sociomateriality: The Use of Actor-Network Theory as a Foundational Theory for Future Research in the Learning Sciences
Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024
International Society of the Learning Sciences
2024

Keune, A., Hurtado, S. & Simšič, Ž.
Exploring AI Ethics through educational scenarios with AI generative arts apps
Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024
International Society of the Learning Sciences
2024

Hurtado, S. & Keune, A.
Social media as a space for youth’s algorithmic resistance
Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024
International Society of the Learning Sciences
2024

Keune, A. & Hurtado, S.
Materializing social media youth practices toward algorithmic learning
Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024
International Society of the Learning Sciences
2024

Keune, Anna
Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration
International Journal of Computer-Supported Collaborative Learning
2024

Keune, A., Ruiz-Cabello, P., Peppler, K., & Chappell, K. (Eds.)
Advancing posthuman methodologies in the study of teaching and learning [Special Issue]
Digital Culture & Education
2023
14.5

Keune, A. & Hurtado, S.
Algorithmic learning while creating and sharing content on social media
Computer science continues to face lopsided access for all youth. Identifying youth practices that can be leveraged for computational learning can contribute to a transformed participation in computer science because a wider range of people can see themselves in this field and are ready to contribute to shaping it. In this research, we investigate social media practices as youth-driven practices and rich contexts for computational learning. We thematically analyzed 13 semi-structured interviews with girls (ages 13-18) in Latin America and Europe that also included social media walkthroughs. We translated the youth-practices on social media into pseudo-code to show the computational depth of their everyday, repeated practices, and, in some cases, mundane practices. We found three youth-driven social media practices that can inform the design of youth-driven computational learning activities. These are: (a) Content sharing as flow control structures, (b) content curation as a loop, and (c) playing with algorithms. We highlight the computational themes with data excerpts to illustrate the possibilities of social media as a context for computational learning. We present implications for the design of computational learning opportunities in the form of connected algorithmic learning workshops that are part of future plans for this research and are promising for broadening computing cultures.
2023

Keune, A., Thompson, N., & Koenen, J.
Conductive chemistry learning: Work in progress submission for the development of a conductive chemistry kit.
Constructionism/FabLearn 2023
2023