Artificial Intelligence in Higher Education I 3301-ZJ-JS003
Part one of the online course to be covered in the winter semester, will address the theoretical foundations of AI, with a special focus on research and educational perspectives.
The course materials aim to equip students with the knowledge, skills, and confidence necessary to optimize their work using this new technology in a highly digitalized society.
A vital component of the course is language training – tasks and activities exploring the lexico-grammatical properties and overt formal markers of genre-specific English used in AI studies (from awareness tasks and text analysis to production and reflection).
Specific course content (subject to updates as the field evolves and in line with participants' interests) includes:
• A historical outline of computer science and its connections with other disciplines.
• Basic terminology related to artificial intelligence and its educational applications.
• Introduction to Generative AI (GenAI).
• An outline of the social and philosophical implications of the development of so-called strong and weak AI.
• Language training (from awareness exercise to application and production).
• Final project presentations (with self-assessment)
Type of course
Mode
Course coordinators
Learning outcomes
Knowledge
Students will know and understand:
• The specifics of the cognitive revolution initiated by the introduction of AI into research and education (K_W01, K_W02).
• the role of GenAI in academic work and foreign language teaching (K_W01, K_W02).
Skills
Students will be able to:
• Utilize the acquired knowledge to carry out a research project on the theoretical foundations of using AI in linguistic research and foreign language learning/teaching (K_U01, K_U03, K_U04, K_U05, K_U08, K_U09).
• Correctly use the terminology and conceptual tools related to AI in the context of language learning and conducting research appropriate for MA studies (K_U01, K_U03, K_U04, K_U05, K_U08, K_U09).
Social Competence
Students will be ready to:
• use AI ethically in academic research and language work (K_K02, K_K03, K_K04).
• apply AI mechanisms in their own academic research and in language education (K_K02, K_K03, K_K04).
Language skills:
• language training at the C2 level (K_U09).
Assessment criteria
Progress on the designated online platform (Moodle) as measured by the range and timely completion of interactive activities, quizzes, open tasks and the final project.
In order to receive a passing grade in this course, the student must receive a passing score (at or above 60% of the total score). The total includes open tasks, quizzes and the final project (to be completed individually, in pairs or teams of three). The same thresholds and criteria apply if make-up work is required.
Bibliography
Flowers, J. C. (2019, March). Strong and Weak AI: Deweyan Considerations. In AAAI spring symposium: Towards conscious AI systems (Vol. 2287, No. 7).
Ghafouri, M. (2024). ChatGPT: The catalyst for teacher-student rapport and grit development in L2 class. System (Vol. 120/2024)
Hawkridge, D. (2022). New information technology in education. Taylor & Francis.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.
Mohebi, L. (2024). Empowering learners with ChatGPT: insights from a systematic literature exploration. Discover Education.
Teng, M.F. (2024). “ChatGPT is the companion, not enemies”: EFL learners’ perceptions and experiences in using ChatGPT for feedback in writing. Computers in education (Vol. 7/2024)
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., ... & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.
Wo, J.H., & Choi, H. (2021) Systematic review for AI-based language-learning tools. arXiv:2111.04455.
Vaswani, A. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762, https://doi.org/10.48550/arXiv.1706.03762
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023, April). Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-21).
+ Tech&Learning resources for the use of Microsoft Copilot in language teaching
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: