Generative Artificial Intelligence (GenAI) is no longer a future possibility; it’s a present reality reshaping higher education. While it challenges traditional teaching and assessment models, it also unlocks unprecedented opportunities for innovation. However, the sector’s response has been uneven, with individual institutions and academics often tackling this new terrain in isolation. This fragmented approach risks weakening our collective influence and exacerbating inequalities among students who may not have equitable access to GenAI tools.
GenAI signals a profound shift in our sector; established methods of teaching and assessment may soon prove inadequate. Some educators have embraced this shift, integrating GenAI into their teaching strategies, while others remain wary or hesitant. In the face of this uncertainty, a key question emerges: what can the average lecturer do without becoming an AI expert or assuming the burden of teaching AI literacy?
This blog seeks to answer that question by proposing a fundamental shift from assessing the final output to evaluating the learning process. By adopting holistic, competency-based assessment methods such as information literacy trails, iterative feedback, and portfolio-based work we can better prepare our students for a world where GenAI plays an ever-growing role. This approach not only aligns with sound pedagogical practice but also ensures we continue to cultivate critical thinking and adaptability skills essential for success in a GenAI-influenced future.
The value of assessing the learning process
Shifting to process-focused assessment offers a practical and inclusive way to adapt to the challenges GenAI presents. By concentrating on how students engage with, evaluate, and critically utilise available tools, we prepare them for workplaces where AI literacy is becoming indispensable. Crucially, this approach also promotes fairness, acting as a bridge until all students can access GenAI resources equitably. Rather than assessing the quality of the tools themselves, we focus on how effectively students use what’s available to them.
Process-based assessment provides a more transparent measure of essential competencies like information literacy, critical thinking, and problem-solving skills that may not be fully apparent in a polished final product. It also allows us to evaluate less tangible but equally important attributes, such as professionalism, ethics, and accountability, which are often overlooked in traditional assessment models. Group-based learning already highlights the benefits of assessing collaboration and process (Francis at al., 2025), offering a strong foundation for this approach.
From a pedagogical perspective, focusing on the iterative nature of learning aligns with best practices. The principle of “assessment as learning” promotes ongoing feedback, reflection, and engagement elements that are central to effective education. This emphasis on interaction not only enhances the learning experience but also mitigates the challenges posed by GenAI (Smith & Francis, 2024).
By assessing the process rather than just the product, we empower students to navigate a GenAI-driven world with critical awareness, creativity, and confidence. As educators, this approach allows us to foster the skills that truly matter, skills that will equip our students to thrive, no matter how technology evolves.
Integrating process-focused assessment without increasing workload
Adopting process-focused assessment need not entail more marking. By applying TESTA (Transforming the Experience of Students through Assessment), academics can develop streamlined, efficient tasks that genuinely enhance learning outcomes. Rather than assessing a single 2,500-word essay, smaller, interconnected assignments allow students to demonstrate progress step by step, supporting deeper development and retaining authenticity.
For example:
- A bibliography of 10 sources, each briefly explained in terms of relevance to the final product
- A fully referenced, 1-page bullet-point plan outlining key arguments and structure
- An 800–1,000-word essay focusing on critical synthesis and evaluation
These scaffolded stages require no more grading time overall yet enrich feedback opportunities and spread effort evenly. Crucially, they offer repeated chances to build student competencies, intensify lecturer–student dialogue, and promote fairer use of GenAI tools. This model thus makes embracing a process-based approach both practical and impactful.
Grading the process
Assessing process-based work is not dissimilar to evaluating critical thinking in traditional essays. Rather than focusing solely on the final output, we focus on the skills and decisions underpinning it. For example, we can evaluate:
- The student’s selection of sources and their justification for these choices.
- Their understanding and critique of the material.
- How effectively they integrate ideas from multiple references to form a coherent argument.
By focusing on these skills, we align assessment with the core competencies we aim to cultivate such as critical thinking, information literacy, and analysis – rather than their surface-level manifestation in a polished product.
As AI literacy becomes embedded within curricula, we can extend this method to assess how students use GenAI platforms for different tasks. This evolution allows us to evaluate not only their technical proficiency but also their critical engagement with emerging technologies, ensuring students are equipped to navigate a rapidly changing professional landscape.
Conclusions
GenAI is undeniably reshaping the landscape of higher education, but it need not undermine the core aims of teaching and learning. By embracing process-focused assessment and prioritising the development of essential competencies, educators can transform this challenge into a significant opportunity.
This approach mitigates the risks of AI-generated work and enriches the educational experience. By preparing students to engage critically and thoughtfully with GenAI, we equip them for a future where such tools are an integral part of professional life. Moreover, by championing human interaction, dialogue, and the cultivation of actual intelligence, we reaffirm the irreplaceable role of educators and the transformative power of higher education.
References:
- Francis, N., Pritchard, C., Prytherch, Z. and Rutherford, S., 2025. Making teamwork work: enhancing teamwork and assessment in higher education. FEBS Open Bio, 15(1), pp.35-47.
- Smith, D. and Francis, N., 2024. Process not product in the written assessment. In Using generative AI effectively in higher education (pp. 115-126). Routledge.
About the author: Dom Henri (University of Hull), Nigel Francis (Cardiff University), and David Smith (Sheffield Hallam University) are all National Teaching Fellows, Principal Fellows of Advance HE, and past winners of the Royal Society of Biology’s Higher Education Bioscience Teacher of the Year award. Their interests include anything that makes higher education more meaningful, engaging and inclusive.
This article has been kindly repurposed from Advance HE and you can read the original here.