AI Literacy for Higher Education: Applying GenAI Responsibly in Your Studies

AI Literacy for Higher Education: Applying GenAI Responsibly in Your Studies

Delivery institution

Faculty of Education and Psychology
Institute of Education

Instructor(s):

Dr. László Horváth

Start date

22 September 2025

End date

21 December 2025

Study field

CHARM priority field

Study level

Study load, ECTS

2

Short description

This online course equips higher education students with essential AI literacy and practical skills to effectively and responsibly use Generative AI (GenAI) tools in their academic studies. Explore AI fundamentals, ethical considerations, practical applications like research assistance and data analysis, and develop strategies for navigating the evolving landscape of AI in academia and future careers.

Full description

AI Literacy for Higher Education: Applying GenAI Responsibly in Your Studies is a 2 ECTS, fully online asynchronous course designed to empower students across all disciplines to navigate the opportunities and challenges presented by Artificial Intelligence, particularly Generative AI (GenAI), within the academic context.
– Module 0. Welcome & Orientation
– Module 1: Introduction to AI – basic concepts, the background of the technology for non-tech people
– Module 2: AI’s impact on society, labour market consequences
– Module 3: Responsible AI use and ethical considerations, challenges associated with GenAI use (from hallucinations to yes-bots)
– Module 4: Effective use of Large Language Models – prompt engineering
– Module 5. Use of GenAI to support your learning process, metacognitive strategies
– Module 6: GenAI for research and literature review
– Module 7: GenAI for data analysis and other applications
– Module 8: Synthesis and reflection – final challenge (develop your own prompt for supporting higher education studies)

The course moves beyond theory to practical application. Students engage directly with real-world academic challenges (e.g., “How can GenAI assist in my literature review responsibly?”, “How can I use GenAI for preliminary data exploration ethically?”). Modules feature practical assignments designed around these challenges, requiring students to investigate, apply tools, and reflect on solutions. We start by exploring AI’s broad societal impact, specifically prompting students in Module 2 to research and discuss AI’s effect on their own future fields, connecting AI concepts directly to diverse disciplinary contexts. The ethical considerations (Module 3) and tool applications (Modules 4-7) are framed within the general context of academic work, applicable across various disciplines. As a fully asynchronous online course delivered via Canvas, it offers maximum flexibility, allowing learners to engage with materials and activities at their own pac

Learning outcomes

Define core concepts of AI and GenAI and explain their basic technological underpinnings.
Analyze the potential impact of AI on the labour market and future career paths relevant to their field of study.
Identify key components of AI literacy and evaluate different models for understanding AI capabilities and limitations.
Apply established frameworks to evaluate the ethical implications and responsible use of GenAI tools in academic contexts.
Recognize potential risks and dangers associated with GenAI, including bias, misinformation, privacy, and environmental impact.
Utilize effective prompt engineering techniques to interact productively with LLMs for various academic tasks.
Employ specific GenAI tools and techniques responsibly to support academic activities such as brainstorming, drafting, literature searching, and basic data exploration.
Critically evaluate the outputs generated by AI tools, recognizing the need for human oversight, verification, and original intellectual contribution.
Develop personal guidelines for the ethical and transparent use of AI in their own academic work, adhering to principles of academic integrity.
Engage in constructive peer review of AI-assisted academic tasks, providing feedback on both the process and the product.

Course requirements

Pre-requisites: basic digital literacy.

Places available

50

Course literature (compulsory or recommended):

Bozkurt, A., & Sharma, R. C. (2023). Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education, 18(2), i-vii. https://doi.org/10.5281/zenodo.8174941
Deloitte AI Institute (2023). Generative AI and the future of work. https://www2.deloitte.com/content/dam/Deloitte/de/Documents/human-capital/Deloitte_GenAI-Future-of-Work.pdf
Furze, L. (2024, November 10). Synthetic Sycophants: Why “Yes-Bots” are a Problem for Education. Leon Furze. https://leonfurze.com/2024/11/11/synthetic-sycophants-why-yes-bots-are-a-problem-for-education/
Hannigan, T. R., McCarthy, I. P., & Spicer, A. (2024). Beware of botshit: How to manage the epistemic risks of generative chatbots. Business Horizons, 67(5), 471–486. https://doi.org/10.1016/j.bushor.2024.03.001
High Level Expert Group on Artificial Intelligence (AI HLEG) (2019). A definition of AI: Main capabilities and disciplines. https://ec.europa.eu/futurium/en/system/files/ged/ai_hleg_definition_of_ai_18_december_1.pdf
Prompt Engineering Guide: https://www.promptingguide.ai/
Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4. Ed.). Pearson.
Spatola, N. (2024). The efficiency-accountability tradeoff in AI integration: Effects on human performance and over-reliance. Computers in Human Behavior: Artificial Humans, 2(2), 100099. https://doi.org/10.1016/j.chbah.2024.100099
Stricker, J. K. (2025, February 25). The Synthetic Knowledge Crisis [Substack newsletter]. The Future of Higher Education. https://jeppestricker.substack.com/p/the-synthetic-knowledge-crisis
UNESCO. (2019). Beijing Consensus on Artificial Intelligence and Education. Outcome document of the International Conference on Artificial Intelligence and Education ‘Planning education in the AI era: Lead the leap’. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000368303
World Economic Forum (2025). Future of Jobs Report 2025. https://reports.weforum.org/docs/WEF_Futu

Planned educational activities and teaching methods:

– Modular content delivery (micro-lectures, curated readings, interactive elements, demonstrations/screencasts)
– Asynchronous discussions: Structured forums prompting reflection, critical debate on ethical dilemmas, and sharing tool experiences.
– Practical application exercises: Hands-on assignments tackling defined academic tasks using GenAI tools, requiring documentation and reflection.
– Peer-review activities: Using Canvas Peer Review for constructive feedback on practical assignments, guided by rubrics.
– Reflective journaling: Short tasks encouraging reflection on learning, evolving perspectives, and personal ethical stances.
– Basic knowledge test (quizz)

Course code

(not yet available, elective course)

Language

Assessment method

Final certification

Transcript of records

Assessment date

7 December 2025

Modality

Learning management System in use

Canvas (mooc.elte.hu)

Contact hours per week for the student:

flexible (asynchronous), workload in alignment with 2 ECTS (60 hours over 13 weeks)

Specific regular weekly teaching day/time

flexible (asynchronous)

Time zone