GenAI and its pedagogical potential

The introduction of generative artificial intelligence (GenAI) has sparked a debate in the higher education sector about its benefits and pitfalls, as well as the strategies to ensure students’ ethical utilisation and authentic learning. GenAI embodies a dual nature: it is a catalyst for more personalised learning experiences and it is a potential disruptor of students’ autonomy and development of critical and metacognitive skills (Fuchs, 2023). The question arises: how can we harness the transformative power of GenAI while safeguarding the authenticity of the learning process?

Scholars have started exploring the pedagogical potential of GenAI, reconceptualising its role, not as a source of knowledge, but as a ‘dialogic agent’ (Tang et al., 2024) fostering critical and personal engagement with learning. By considering GenAI as a ‘critical friend’, students are enabled to engage with knowledge acquisition and application as they receive feedback, are challenged in their conclusions, and receive guidance tailored to their individual learning trajectories. Chan and Hu (2023) also show that students are becoming increasingly aware that GenAI outputs are not flawless, and, therefore, must be reviewed and refined by fact-checking and further exploring the content.

To this end, I suggest that students would need to use what Nicol (2020) terms ‘internal feedback’: the internal mechanism that we use to compare our own work with others’ work. Nicol found that, when students compare their work, not with teachers’ feedback but with external sources of knowledge, they develop autonomy and learn more effectively. It would seem that reviewing GenAI outputs against sources of knowledge other than teachers might enhance students’ autonomous learning.

GenAI and intended learning outcomes

If students can use GenAI as a ‘critical friend’ while also being critical of its outputs, by comparing them with authentic sources of knowledge, the question becomes whether the current uses of GenAI enable students to learn. Reviewing the grey and initial research on the use of GenAI for learning, mainly ChatGPT, it seems that the focus is on the role that GenAI plays in activities rather than on teaching objectives, that is the pedagogical rationale for using GenAI. Michalon and Camacho-Zuñiga (2023) found that the use of ChatGPT enhanced students’ writing, critical thinking and logical reasoning skills, of which all are fundamental educational aims in higher education.

‘To ensure that GenAI is used to build educational experiences, it could be helpful to start from the institutional graduate learning outcomes, that is the expectations of how graduates will think and behave as citizens and professionals. Only after having defined the learning outcomes we want to achieve, can we start planning the role that GenAI will play with the student.’

As a teacher and teacher educator, I know that teachers planning their teaching have intended learning outcomes in mind, which are aligned with the learning outcomes of their modules, courses and institutions. To ensure that GenAI is used to build educational experiences, it could be helpful to start from the institutional graduate learning outcomes, that is the expectations of how graduates will think and behave as citizens and professionals. Only after having defined the learning outcomes we want to achieve, can we start planning the role that GenAI will play with the student.

Graduate learning outcomes/attributes consist of knowledge, values, attitudes and skills that students develop during their studies. Wong et al. (2021) examined graduate attributes as they are defined by UK universities and identified four discourses framing graduate attributes: self-awareness and life-long learning; employability; citizenship; and academic/research literacy. These discourses include a variety of graduate learning outcomes that are pedagogically sound, well defined, specific and measurable. Using graduate learning outcomes as educational objectives to frame GenAI-based activities, instead of limiting ourselves to deciding the role GenAI should play in teaching activities, could enhance the educational value of GenAI and enable teachers to use it purposefully to support students’ authentic learning experience.

References

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(43). https://doi.org/10.1186/s41239-023-00411-8

Fuchs, K. (2023). Exploring the opportunities and challenges of NLP models in higher education: Is Chat GPT a blessing or a curse? Frontiers of Education, 8, 1166682. https://doi.org/10.3389/feduc.2023.1166682

Michalon, B., & Camacho-Zuñiga C. (2023). ChatGPT, a brand-new tool to strengthen timeless competencies. Frontiers of Education, 8, 1251163. https://doi.org/10.3389/feduc.2023.1251163

Nicol, D. (2020). The power of internal feedback: Exploiting natural comparison processes. Assessment & Evaluation in Higher Education, 46(5), 756–778. https://doi.org/10.1080/02602938.2020.1823314.

Tang, K.-S., Cooper, G., Rappa, N., Cooper, M., Sims, C., & Nonis, K. (2024). A dialogic approach to transform teaching, learning & assessment with generative AI in secondary education. http://dx.doi.org/10.2139/ssrn.4722537.

Wong, B., Chiu, Y.-L. T., Copsey-Blake, M., & Nikolopoulou, M. (2021). A mapping of graduate attributes: what can we expect from UK university students? Higher Education Research & Development, 41(4), 1340-1355. https://doi.org/10.1080/07294360.2021.1882405