Title: Beyond the Textbook: Rethinking Students’ Competencies in the LLM Era

Author: Natalie Kiesler

Abstract:
In an era where Large Language Models (LLMs) redefine the educational landscape, the development of new competencies for students has become paramount. This presentation introduces two research studies. The first one aimed to explore the potential of LLMs like ChatGPT to generate formative feedback to students' queries regarding introductory programming tasks. The second study evaluated the performance of ChatGPT in introductory programming tasks and its implications for assessments. The presented studies are used as a basis to discuss the challenges and opportunities of generative AI in higher education, comprising new support options for learners but also misleading information. The conducted research further helps derive competency components (meaning knowledge, skills and dispositions in context of a task) students must cultivate to thrive in programming education and their future careers. LLMs are forcing institutions and educators to rethink their curricula and learning objectives, as they highlight the importance of critical thinking, and the ability to evaluate information and may redefine dispositions such as adaptable, self-directed, and professional. Students need to develop the competencies to not merely rely on LLMs but to engage with them as tools with both opportunities and limitations. The session will close by inviting participants to reflect on the evolving role of educators as facilitators of competency development in the LLM era.

Referenced studies:
Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessments. https://doi.org/10.48550/arXiv.2308.08572
Exploring the Potential of Large Language Models to Generate Formative Programming Feedback. https://doi.org/10.48550/arXiv.2309.00029