by Dr Sarah Watson and Kamila Bateman, Academic Developers in Educational Enhancement

A process approach in teaching is not a new concept. It was first introduced by Stenhouse in 1975 as an alternative to the product model. It concentrates on teacher activities, learner activities and the conditions in which learning takes place. In focusing on the nature of learning experiences, rather than specific learning outcomes, the process model emphasises means rather than ends.

Although it predates the age of artificial intelligence, can Stenhouse’s approach offer a fresh perspective on AI in education?

This post highlights how academics at the University of Sussex and beyond are adopting process-oriented approaches to assessment, not only to reduce over-reliance on AI, but, more importantly, to strengthen the pedagogical purpose and value of assessment for students.

Shifting from ‘product’ to ‘process’

Shifting the focus from product to process allows us to foreground thinking, development, and learning. It also provides room to explore how AI might support students during this process, rather than replace it.

One effective approach is embedding writing practice directly into teaching. At Sussex Law School, Verona Ní Drisceoil (2023) describes dedicating ten minutes of each seminar to structured writing activities. This helped students develop their writing skills incrementally and prepare for end-of-term essays. Student feedback was overwhelmingly positive, with many reporting that the practice demystified academic writing.

Teaching on the foundation year at the University of Sussex, Sue Robbins (2023) similarly argues that when students understand the writing process, the perceived threat of generative AI diminishes significantly. Embedding academic skills into teaching therefore acts as a powerful deterrent to academic misconduct. As Robbins notes, our choices in response to AI are to avoid it, outrun it, or adapt to it. Given the rapid development of generative AI, we have a responsibility to support students in learning how to use these tools responsibly, both during their studies and beyond.

Another productive strategy is encouraging students to treat AI as a writing coach rather than a content generator. Alicja Syska (2025) suggests using AI as a tutor that prompts critical thinking and supports students in producing their best work, without doing the work for them. She advocates collaborative writing in the classroom and rethinking assessment criteria to emphasise original thinking, writing development, and opportunities for peer review.

Fostering engagement with the learning and assessment process

One approach to help students engage with process is to design marking rubrics that explicitly reward idea development and provide opportunities for peer review. Bianca A. Simonsmeier et al (2020) note that such approaches support active, self-directed learning and encourage social interaction and reciprocal teaching, whether through online discussion forums or structured peer assessment.

We can also diversify assessment formats beyond the traditional essay. Introducing reflective components, small-group critical evaluation, collaborative planning, or playful elements can increase engagement and ownership. Denise Wilkinson (2024) suggests using “flipped assignment” techniques, interactive engagement tasks, and collaborative reflection to help students feel more invested in their work. This emphasis on ownership is echoed by Helen Foster (2024), who highlights the role of formative assessment in supporting self-regulated learning and creating more inclusive learning environments.

Another opportunity lies in building on what students already know about AI and how they use it. Tim Requarth (2025) advocates assignment-specific guidance that supports a balanced approach to AI use, neither punitive nor overly permissive.

In the Economics department at the University of Sussex, Gabriella Cagliesi (2025) and Carol Alexander (2025) have taken this further by developing customised ChatGPT tools that store module content and are fully integrated into the learning process. These tools function as trusted study aids, enabling students to engage critically with course material.

Cagliesi and Alexander found that their custom GPTs allowed students to explore, question, and critique content outside of class, creating more space during teaching sessions for relationship-building, personalised support, and meaningful discussion.

The rise of generative AI reinforces something we often overlook: Meaningful learning happens through human connection and collaboration. As Syska suggests, thoughtfully integrating AI may allow us to reclaim time and space for deeper engagement with learning and for valuing what we bring as human educators and learners in a digitally dominant world.