The agenda for teaching and learning in higher education has been transformed by the emergence of AI. University teachers and leaders have begun to reorganize and rewrite how they teach and assess student work. The idea of abandoning AI in education, as some nations still do, will not solve the situation. It is well known that innovation cannot be reversed but at least steered in a direction that uses its potential constructively. Instead, most educators are aware that integrating AI into their studies is a possible way forward for the sake of all parties, students and teachers (Tchibozo; in press). One of the overarching learning goals is the appropriate, reflective and critical use of GenAI for all parties involved. A systematic review identified five main environments for applying AI in higher education: (1) assessment/evaluation, (2) prediction, (3) AI assistant, (4) intelligent tutoring system (ITS), and (5) student learning management. In terms of academic fields, it is interesting to note that education as a research affiliation was the highest at 28% (Crumpton & Burke, 2023).

Taking a closer look at the day-to-day challenges in the classroom, it is necessary to emphasize the individualized use of AI as, for instance, intelligent tutoring (Atlas, 2023, p. 79). Analyzing current studies on the challenges and key benefits of AI (Rasul et al. 2023), one need to conclude that adapted learning and individual feedback is the key one can subsume as AI assistant (Crumpton & Burke, 2023). The challenges hereby are ethical and equity considerations as well as academic integrity and finally the potential of falsified information (Rasul, 2023, p.10). Therefore, one idea is to consider how to make students critical constructive, and creative users for the sake of their own learning.

The approach in this study is to redo the presumably "analogous" student work done in the classroom before. On the one hand, "redoing" in this approach is to be understood as an intentionally created learning opportunity for students through the need to reconsider what the core focus of the task is through the necessity of writing an appropriate and productive prompt. On the other hand, students have to re-evaluate the logic of their previously constructed own results with the results of the prompts and are also fostered to verify their credibility. 

Since it is impossible to control what students do outside the classroom, this quasi-experimental design of doing and redoing inside the classroom will contribute to create better observable conditions. Since teaching is ideally seen, a genuine dialogical activity, it is also necessary to emphasize the role of teachers and their pedagogy competencies to create an appropriate teaching framework and to apply methods for this kind of lecturing.

The three main reserach questions are:

1. How do students experience the added value of using AI in the classroom as a support tool?
2. How does the re-doing of learning tasks supported by AI contribute to students' critical thinking and learning success?
3. What does the teacher learn from the instruction to use AI as a critical constructive tool for student learning?

The methodological approach is informed by classroom-based inquiry in higher education at the bachelor level. This approach will be applied over one semester in two countries and two different educational disciplines, using the reflective questions listed below. The main research intention of each item is mentioned in parathesis Given the situation of using AI as an individual assist-tool for student learning, then the reflective questions to the students need to be at a level that makes them open enough to comprise the variety of tasks one can encounter in everyday teaching situations, as well as tasks that we, as teachers, do not yet see as requiring an assistant. On the other hand, one has to think about what these research questions want to explore. Therefore, a qualitative open method is used, which is also structured enough to draw conclusions across all the individual students' reflections. It will be used as well for the teachers to reflect on after each lecture. a- What did the AI do well or not? (Value) b- What shortcomings did you discover? (Critical Thinking) c- What parts of the AI tool's response did you find new or interesting? (Knowledge acquisitions and Insights) d- Advice you would give to a classmate or colleague about using AI for the task? (Guidance for peers) e- What was the most difficult part for you about using AI for this task/lecture? (Challenges) f- How do you verify that the results generated by AI are correct? (Verification) By following the six steps of Clark and Brown’s (2006) thematic analysis approach it will be possible that we can derive central topics from these students’ feedback and critical reflections. The method emphasizes remaining open to emerging ideas in the data rather than forcing responses into preset categories, thus capturing a rich, nuanced understanding of how students engage with and perceive AI as an educational tool and what they perceive critical.

This classroom-based research study is a work in progress. A small pilot study conducted last semester showed that the questions asked, and answers given by the students were quite close to the topics of the educational subject, therefore the critical perspective was difficult to elicit through the response given by the students. Therefore, the (reflective) questions will be rewritten and expanded and will be action–research oriented (Niff, 2017) incrementally improved and actualized. One finding that can already be concluded is that the students were engaged in exploring what the AI will answer to their prompts in comparison to their own performances and results. This comparative, evaluative process induced by the redoing of the tasks is presumably a way to enhance taking a critical role and thus as well learn and acquire new subject knowledge. Another result is that the paradigm of self-regulated learning and of exploring and learning together as partners, which can be found in adult education, will be more emphasized. This is in line with the principles of andragogic (Loeng, 2020), a shift towards collaborative learning that is inquiry-based learning sensu Dewey as he mentioned the value of the community of inquiry, where teachers will take the role as advisers but also as co-learners.

Atlas; S. (2023)."ChatGPT for Higher Education and Professional Development: A Guide to Conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548 Braun, V. & Clarke, V. (2006) Using thematic analysis in psychology, Qualitative Research in Psychology, 3:2, 77-101, DOI: 10.1191/1478088706qp063oa Crompton, H. & Burke. D. (2023). Artificial intelligence in higher education: the state of the field: IN International Journal of Education Technology in Higher Education 20(22);1-22. https://doi.org/10.1186/s41239-023-00392-8 Boydston, J. A. (ed.) (1969). The Collected Works of John Dewey, 1882-1953 (37 Volumes). Southern Illinois Up. Loeng, S. (2020). Self-Directed Learning: A Core Concept in Adult Education, Education Research International, 2020, 3816132. https://doi.10.1155/2020/3816132 McNiff, J. (2017). Action research: All you need to know. Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., Sun, M., Day, I., Rather, R. A. & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. Tchibozo, G. (in print). Education and Artificial Intelligence. Springer.