Tag: case study

Case Study 3: Assess and/or give feedback for learning

Contextual Background 

Most core users of the Grow Lab have no background in science or laboratory practices. This is not, an impairment for anyone to work in the lab, for we provide inductions, training and support continuously. However, the use of AI to complete planning forms, generate protocols or come up with project ideas, frequently undermines my ability to assess students’ actual understanding. This creates a level of distrust, for it can be a safety concern.

Evaluation

Technician assessment is typically informal and dynamic than traditional academic ones. Although we have a Grow Lab form that acts as scaffolding (Vygotsky 1978) for student organization prior to lab sessions, this can be completed using AI.

Since our primary assessments happen through face-to-face interaction with students in the lab, we can get information from them directly, and assess AI influence. We attempt to do what Wiggins (1990) calls authentic assessment by observing their confidence, language, and experimental plans. We are assessing for their theoretical domain of the subject through their practice, but also, for tacit knowledge (Polanyi 1966).

AI has limitations and does not take our specific context in consideration. When a proposed work is not compatible with the student’s comprehension of the topic, we suspect of AI use. The more a student masters issues related to their project, practical and theoretical, the more they will develop autonomy in the lab. Their independence signals the successful learning and the effectiveness of our approach.

Moving forwards

Our main strategy must be to be constantly aware, to question without intimidating, continuously ask for their sources, and assess their understanding, while allowing progressively more independence. If we suspect of AI use, we ask for an original non-AI source, go through the original finding versus the AI response, discussing its limitations and possible safety issues.

Moving forward, I will look into creating tools guiding students through good AI use for laboratory practices, giving examples of health and safety and intellectual property concerns and highlighting UAL guidelines on AI (UAL, Nd). I have been approaching the topic through staff development workshops on AI and academic integrity and contacting colleagues from other laboratory institutions (such as RCA) to discuss how they approach the subject. One common strategy is to have “checkpoint questions” during consultations and conversations like “walk me through your plan” and “what happens if this fails”. We currently do it intuitively during conversations but might be a good idea to present them more frequently as provocations for critical reflection – in the Grow Lab form, inductions or other tools.

We cannot provide the intensive support that we would like as technicians, which would allow on our part also, a better understanding of student’s comprehension, limitations, needs and a more tailored support to each. The solution, as suggested by Rowe and Potier (2026) in their AI workshop, is that students get more “time in the physical world”, or in this case, more time in the lab environment. If we cannot provide them with more time, we need to work on providing the best quality in the time they have.

References
Wiggins, G. (1990) ‘The case for authentic assessment’, Practical Assessment, Research, and Evaluation, 2(1), Article 2. Available at: https://doi.org/10.7275/ffb1-mm19

Vygotsky, L. S. (1978) Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.

University of the Arts London (no date) AI and Arts Education. Available at: https://www.arts.ac.uk/about-ual/learning-and-teaching/digital-learning/ai-and-education (Accessed: 22 March 2026).

Rowe, C. and Potier, R. (2026) ‘Art, Design and Artificial Intelligence’ [Guest lecture]. PGCert in Academic Practice, University of the Arts London, 19 March.

Polanyi, M. (1966) The Tacit Dimension. London: Routledge & Kegan Paul (cited in Cleary, V., 2026).

Case Study 1: Knowing and responding to your students’ diverse needs. 

Background

The Grow Lab is a science laboratory, used exclusively by students from three master’s courses and PhD programmes. These students come from diverse professional backgrounds, usually with little or no experience scientific methods and laboratory work. The primary challenge is to support these students in conducting independent laboratory work safely and confidently.

Evaluation

We try to follow a problem-based style of instruction (Domin, 1999) where students apply their knowledge to design experiments and procedures. This means that, to work in the lab they must propose an idea, research literature, suggest a method or feasible in the space, execute, draw conclusions and refine it for the final project.

Our main strategy to address these challenges is the “scientific consultation” – a bookable 20-minutes one-to-one session between students and technicians to discuss their needs, questions and elaborate plans and protocols.

We provide a series of online resources (equipment, material, organisms lists, guides protocols) to support their planning and lab sessions. Additionally, we created a step-by-step form covering essential elements of a lab experiment, organized by sessions.

When students come to a lab session, we will teach and demonstrate procedures, supervise their work and discuss situations that arise. My personal approach is to always ground scientific explanations in something familiar such as their senses, personal interest or professional background, a form of tacit knowledge (Polanyi 1966).

Student’s feedback consistently praises and highlights the consultation model. However, we have limited weekly slots. Student’s most common complaint/request is for more consultations available per week.

Moving forwards

Reflecting on our practice, there are three basic strategies I would like to implement:

  • Foundational laboratory sessions
    • New mandatory inductions for all first-years covering basic laboratory
    practices
  • Practical introduction of the physical space and rules
    • Dependant on alignment with courses, very demanding of technicians
    • Has been agreed but not implemented yet
  • Consultation days arranged with courses
    • We don’t have the capacity to do more consultations (12/week).
    • Close the lab for a couple days in the beginning of the term, an create days fully dedicated to consultations – no lab work, 20 consultations in a day
    • We would support more students in the beginning of a project or brief, avoiding bottlenecks and delays of activities awaiting for an available bookable slot
    • Weekly slots still available for follow ups and new ideas.
    • First test with one course was very successful, being praised and receiving positive feedback by students
    • Intend to expand to other courses
  • AI Integration in science consultation
    • Students increasingly use AI for research and protocol development, leading to inaccurate and sometimes dangerous information and proposals
    • Rather than discourage, would like to incorporate AI prompting during consultations
    • Critically evaluate suggestions together
    • Create/ reffer a guide for good AI use for laboratory practices with good vs bad prompting examples
    • Enforce UAL guidelines for AI (UAL, nd)
      • Link best AIs to be used, and other resources

This reflection clarifies the importance of extending my role beyond answering questions to developing scientific independence. As elaborated by Cleary (2024), the regular conversations between student and technician have a fundamental role in development of critical reflection and thinking. Future practice will emphasize structured support focused in autonomy in the science for the creative exploration.

References 

Domin, D.S. (1999) ‘A review of laboratory instruction styles’, Journal of Chemical Education, 76(4), pp. 543–547.

Cleary, V. (2026). Thinking through making: What kinds of learning take place when HE students engage with creative arts technicians? Art, Design & Communication in Higher Education, 25(1), pp. 7–26. https://doi.org/10.1386/adch_00087_1

Polanyi, M. (1966) The Tacit Dimension. London: Routledge & Kegan Paul (cited in Cleary, V., 2026).

University of the Arts London (no date) AI and Arts Education. Available at: https://www.arts.ac.uk/about-ual/learning-and-teaching/digital-learning/ai-and-education (Accessed: 22 March 2026).