PRISM studies how the students’ answers to short open questions, posted at the start or end of a class, reveal their shared understanding of a subject. For example, students may be asked to summarise the core concept of the last lecture in a few sentences. Dr Kella has been working with the RSM IT department, RSM Digitalisation & Information Services (RDIS) and the technical infrastructure teams at EUR to align PRISM, the Pedagogical Responsive Insights for Scalable Metacognition project, with the university's data privacy requirements.
The PRISM project will be piloted across several RSM courses, including Diversity, Inclusion & the Future of Work (BSc/IBA year 3), Qualitative Research Methods (MSc IM/POC), and Sparking Social Change (MSc POC). This will happen before the results are shared through the CLI's Education Lab and, long-term, released as an open-source tool for use across faculties.
Turning handwritten student responses into a working feedback loop
PRISM makes use of entrance tickets and exit tickets in the classroom. These are well-established classroom teaching tools: at the start or end of a class, students answer a short, open-ended question that is projected or posted at the front of the classroom to gauge either their existing knowledge or to check whether they have understood their homework. Students’ answers give the teacher insight into the class’s collective understanding. This quick interpretation is something that a poll or multiple-choice format typically cannot capture, because these formats often limit students to pre-set answers.
The entrance and exit tickets are handwritten, which drives the screen-free education direction. PRISM processes the full set of tickets for a course. This gives teachers and lecturers a structured picture about where understanding is solid, where it is weak, and where confusion may be spreading. It also assesses depth, which helps to surface superficial or free-riding answers – responses that indicate a student has not engaged with the material – rather than rewarding participation alone. In turn, students receive individual feedback on their own response as well as an indication of how the class performed overall.
Why handwriting still matters
The PRISM project keeps these tickets handwritten and in-class, for two reasons. First, generative AI has made it harder to establish whether a piece of writing reflects a student's own understanding. Handwritten, in-the-moment responses are one of the more reliable signals available. Second is the cognitive rationale: electroencephalogram (EEG) research shows that handwriting engages memory-related brain activity that typing does not, and its slower pace requires students to condense and reframe ideas rather than transcribe them. This is a mechanism that is already associated with handwritten note-takers outperforming typists on conceptual understanding.
Current limitations
At the moment, teachers and/or lecturers must read the exit tickets and turn them into a revised plan before the next class, sometimes within a day. Similarly, entrance tickets can be read in the few minutes it takes for the students to settle into a room, before teaching begins. In a small lecture class, this exercise is manageable. It becomes more difficult in a lecture with 100 or more students, because reading and interpreting 100 handwritten responses between classes is time-consuming. In that way, teachers or lecturers are often left cold-calling a handful of students rather than knowing whether and how the class has understood the material, and with no individual or combined feedback on the class’s overall understanding for the rest of the information. Additionally, if students realise that their tickets have not been reviewed, they start to cut corners and stop believing in the activity itself.
Getting it into instructors' hands
While the current pilot of the PRISM project centres on handwritten entrance and exit tickets, the same approach extends to any open-ended student writing – typed or handwritten – including weekly reflections such as those in the Diversity, Inclusion & the Future of Work course. All usage remains strictly formative, for both feedback and learning rather than just grading, which is in line with emerging EU AI regulations.
Dr Kella is an active user of large language models (LLMs) and artificial intelligence (AI) and holds a centrist view, moving from pro AI, towards LLMs. Together with the team from RDIS, he has developed various tools, games and simulations to use in the classroom, to help enhance student understanding of complex topics and deeper learning.