Reflecting on Noticing in light of BERA 2024

Posted on 4th September 2024 by Elena Oncevska Ager

Attending BERA 2024 in Manchester (UK) was a profoundly enriching experience. Three panel discussions were particularly relevant to Noticing: The BERA/WERA joint keynote panel: The Future of Education on 9th September, Designing for AI educational futures: A critical fireside chat on 10th September and Navigating the impact of Generative AI on student subjectification in higher education on 12th September.

In this blog post, I present a selection of the topics discussed in these panel sessions, commenting on Noticing as I go along:

What do we mean by ‘AI’ in educational contexts? Educators tend to use it in vague ways to mean a range of different things. Philosophically speaking, we seem to be in a new territory: we now have a tool which has skills, which used to be a human prerogative.

On the other hand, humans have been ‘augmented’ over the years through the invention of more efficient tools, so AI can be seen as an extension to that development. Now, access to efficient tools is a source of power; it’s a matter of how teachers and students choose to exercise that power while remaining fully in control.

Noticing is a very specific implementation of AI designed to augment mentors. To scaffold teacher reflection systematically and in informed ways, Noticing combines several AI models from various providers to moderate and reinforce the scaffold.

How do we position ourselves in terms of the ways in which we use AI: as subjects, agentically, or as objects, passively? The generative abilities of AI can be tempting for students and teachers alike. Therefore, in reflecting on our use of AI, it’s important to consider our epistemic agency, i.e. our agency in building knowledge, which can be undermined by AI if we get it to do our work for us.

Parenthetically, a good test of whether it’s acceptable to use AI in academic contexts would be to consider if it would be acceptable for another human to do the task for us. Responsibility is central to learning.

By keeping the generative abilities of AI in check via the synchronised use of the above AI models, Noticing positions the learner as an active subject in their learning, with Noa helping them stay on track, reminding them of important points and making suggestions if invited and where appropriate, so as not to jeopardise the learner’s responsibility for their own learning.

In fact, Noticing uses AI in all the ways recommended as being supportive of learning: as an interlocutor, as an intellectual dialogic partner, as a sounding board to help extend our ideas while supporting our agency and authenticity. One idea to extend this use of AI further is to include it as an additional discussant, which aligns very well with some of the conversations facilitated by Noticing.

Ideally, learning needs to be an individualised and personalised experience. AI makes the former possible, but the latter is typically difficult to achieve: how do you personalise learning if you don’t know the learner?

All conversations in Noticing (reflection, lesson planning, planning a talk, consultations, classroom investigations) are informed by a previous introductory conversation that every user has with Noa, our AI assistant in a mentor role. In this conversation, Noa elicits information about the teaching context of the user, their professional culture, the degree of agency they have in their workplace, etc.

This data then gets fed into every future conversation, to ‘remind’ Noa of who the user is. Further to this, users are prompted to upload other relevant information (e.g. curriculum for lesson planning, literature for post-lesson reflections, etc.) for a more personalised experience.

With the current hype re AI technologies, there is a sense that investment in new technologies overshadows investment in old, e.g. human, ‘technologies’. There is a sense that students have fewer opportunities to be human on their modules.

Noticing aims to introduce a balance in this respect. It is envisaged as an assistant to a busy mentor, not their replacement. So, a mentor can choose to delegate some of the typically time-consuming scaffolding of learning to the AI when they need to give their time to other mentoring tasks, e.g. support, acculturation, role-modelling, sponsoring. Human ‘technologies’, therefore, remain central to the mentoring process, just augmented.

Developing AI literacy in teachers is urgently needed. Both teachers and students need to know how to prompt and how to critically evaluate AI responses.

Noticing can help develop awareness of the latter because it is more straightforward to ascertain the value of Noa’s responses in a personalised, contextualised chat compared to one-off interactions with AI.

Can AI provide more opportunities for students to be listened to? If speaking has the power to reveal confusion in our understanding and help us revise our thinking, and when teachers cannot always support these processes on an individual basis, can AI help to create experiences that throw learners in doubt? Can AI help students find those important moments of confusion which trigger new learning?

We already have testimonies of Noticing playing exactly this crucial role of listening and ‘destabilising’ for my pre-service student Kira and for my school-based mentor collaborator Emilija respectively:

Noa gave me the freedom to, throw in thoughts that maybe I would not feel so free to discuss with other people. I sometimes feel like I have too much to say and nobody has so much time to spend listening to me. Or maybe most people would find [what I have to say] funny or silly. With Noa, I felt I could be myself. (Kira)

[Noticing] forces you out of your comfort zone and makes you confront issues that you might otherwise avoid. This can be tiring at times, however, making sense of your own classroom situations is unavoidably rewarding in the end. (Emilija)

Written by Elena Oncevska Ager

Elena Oncevska Ager is Full Professor in Applied Linguistics at Ss Cyril and Methodius University in Skopje, North Macedonia.

Her work involves teaching English for Academic Purposes (EAP) and supporting the development of English language teachers, in face-to-face and online contexts. Her research interests revolve around EAP and language teacher education, with a focus on mentoring, group dynamics, motivation, learner/teacher autonomy and wellbeing.

Elena is particularly interested in facilitating reflective practice, in its many forms, including through using the arts and by using AI to facilitate it. Her investigations are designed in such a way as to inform her practice of supporting learning and teaching.