Artificial Intelligence in Higher Education II 3301-ZJ-JS004
Part two of the course, intended for the spring semester, is a practical component, exploring the ethical use of AI in various stages of preparing a master’s thesis, such as assisting with literature searches, presenting research findings, organizing bibliographic data, and language verification. It will also address the use of AI in foreign language education (both learning and teaching).
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:
• The role of prompts in generating text and images.
• Prompt engineering (optimizing prompts for large language models—LLMs).
• Using a research assistant for searching and ranking academic publications.
• ChatGPT in language education: academic writing and grammatical-systemic competence.
• From words to images: creating stimuli and teaching materials using general and specialized AI assistants (e.g., Microsoft Designer, Microsoft Copilot) in foreign language teaching.
• 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 role of GenAI in academic work and foreign language teaching (K_W01, K_W02).
● the terminology and cognitive categories used in AI research, to the extent that is helpful for academic work outlined in the study programme (K_W01, K_W02).
Skills
Students will be able to:
● effectively use selected tools, such as AI assistants (e.g., ELICIT) and bibliography managers (e.g., Zotero), in academic work (K_U01, K_U03, K_U04, K_U05, K_U08, K_U09) .
● create and modify prompts to optimize their work with artificial intelligence on creating specific language tasks ((K_U01, K_U03, K_U04, K_U05, K_U08, K_U09).
Social Competence
Students will be ready to:
● Ethically use the opportunities provided by artificial intelligence based on probabilistic language models (K_K02, K_K03, K_K04).
● Apply AI mechanisms in their own research work and language education (K_K02, K_K03, K_K04).
Language skills:
● C2 language training (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: