BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CiviCRM//NONSGML CiviEvent iCal//EN X-WR-TIMEZONE:Europe/London METHOD:PUBLISH BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20260630T130000 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:CiviCRM_EventID_804_c1ec699f7b9af3c31b962e2084dfe9d6@srhe.ac.uk SUMMARY:AI-agnostic Assessment in Biomedical Sciences X-ALT-DESC;FMTTYPE=text/html:

Overview

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< span style="font-size:12pt\;">GenAI ha s unsettled an assumption that well-formed academi c writing signals a learner’s understanding. When a chatbot can write a plausible answer to many typ es and topics of questions\, and when detection to ols struggle to identify AI-generated text with an y consistency¹\, the link between a ‘go od submission’ and ‘a learner’s own grasp of the w ork’ becomes less secure. We need to ask what asse ssment is for and what kinds of learner capability universities ought to recognise when the producti on of fluent writing is no longer a meaningful ind icator on its own.

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We propose a philosophical case for ‘AI-a gnostic’ assessment design\, where assessment rema ins valid and defensible\, whether learners use ge nAI or not. In this framing\, marks are associated with judgement in context: taking a stance\, weig hing explanations\, justifying choices and making the limits of evidence explicit. Our core claim is that these are precisely the moves that different iate human understanding from the pattern-matching of LLMs\, especially where moral\, methodological or situational justification is required. The poi nt is to ensure that it is the learner’s accountab le decision-making that is rewarded.

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AI-agnostic assessment treats learning as something that develops over ti me and in dialogue\, not something captured in a s ingle artefact. Where learners must carry argument s through stages\, respond to critique and explain why they changed their mind or refined a claim\, their agency becomes easier to see and easier to a ssess. This matters across disciplines\, because t he content of a good justification differs by fiel d\, but the need to demonstrate how conclusions we re reached is widely shared. Designing assessment that includes ambiguity and even contradictions\, pushes learners into interpretation rather than re production\, making it harder for any submission\, AI-generated or not\, to slide by on generic cohe rence alone.

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This way of thinking changes what we mean by ac ademic integrity and fairness - it becomes less ab out proving who typed words and more about making responsibility clear. One answer is to treat openn ess about AI use as normal academic practice rathe r than as a confession. Learners can be asked to s tate what tools they used\, how they used them and what they did to change\, check or reflect the ou tputs. This matters ethically because strict bans and unreliable detection can create uneven risk\, with some learners more likely than others to be w rongly suspected². A focus on transpare ncy\, by contrast\, gives everyone the same clear expectations and allows markers to assess what act ually matters – whether the learner can explain th e decisions they made\, the evidence they relied o n and the limits of their argument. In short\, it shifts the important questions from ‘who wrote thi s’ to ‘who is taking responsibility for the judgem ents in it\, and can they justify them?’

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1. Weber-Wulff D\, Anohina-Naumeca A\, Bjelobaba S\, et al. Testing o f detection tools for AI-generated text. Int J Educ Integr. 2023\;19(1):26.

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2. Liang W\, Yuksekgonul M\ , Mao Y\, Wu E\, Zou J. GPT detectors are biased a gainst non-native English writers. Patterns (N Y). 2023\;4(7):100779.

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Speaker bio< /span>

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< span style="font-family:Aptos\, sans-serif\;">Dr Donna Johnson is the Course Director for Postgraduate Biomedical Scienc es at Leeds Beckett University\, where she’s overs een MSc provision since 2015. She’s a Senior Fello w of the Higher Education Academy and a Chartered Science Teacher. Much of her work centres on curri culum design that promotes student agency and supp orts learners from a range of academic and profess ional backgrounds. She’s especially interested in equipping postgraduate students to think criticall y and work independently\, while still offering st ructured\, inclusive support. Her teaching draws h eavily on authentic assessment and skills developm ent\, particularly in communication\, applied rese arch\, and data interpretation. Alongside teaching \, she’s led a series of research projects explori ng learning innovation in biomedical science.

DESCRIPTION:Overview\n \n \n \n GenAI has unsettled an assumpt ion that well-formed academic writing signals a le arner’s understanding. When a chatbot can write a plausible answer to many types and topics of quest ions\, and when detection tools struggle to identi fy AI-generated text with any consistency1\, the l ink between a ‘good submission’ and ‘a learner’s o wn grasp of the work’ becomes less secure. We need to ask what assessment is for and what kinds of l earner capability universities ought to recognise when the production of fluent writing is no longer a meaningful indicator on its own.\n \n \n \n We propose a philosophical case for ‘AI-agnostic’ ass essment design\, where assessment remains valid an d defensible\, whether learners use genAI or not. In this framing\, marks are associated with judgem ent in context: taking a stance\, weighing explana tions\, justifying choices and making the limits o f evidence explicit. Our core claim is that these are precisely the moves that differentiate human u nderstanding from the pattern-matching of LLMs\, e specially where moral\, methodological or situatio nal justification is required. The point is to ens ure that it is the learner’s accountable decision- making that is rewarded.\n \n \n \n AI-agnostic as sessment treats learning as something that develop s over time and in dialogue\, not something captur ed in a single artefact. Where learners must carry arguments through stages\, respond to critique an d explain why they changed their mind or refined a claim\, their agency becomes easier to see and ea sier to assess. This matters across disciplines\, because the content of a good justification differ s by field\, but the need to demonstrate how concl usions were reached is widely shared. Designing as sessment that includes ambiguity and even contradi ctions\, pushes learners into interpretation rathe r than reproduction\, making it harder for any sub mission\, AI-generated or not\, to slide by on gen eric coherence alone.\n \n \n \n This way of think ing changes what we mean by academic integrity and fairness - it becomes less about proving who type d words and more about making responsibility clear . One answer is to treat openness about AI use as normal academic practice rather than as a confessi on. Learners can be asked to state what tools they used\, how they used them and what they did to ch ange\, check or reflect the outputs. This matters ethically because strict bans and unreliable detec tion can create uneven risk\, with some learners m ore likely than others to be wrongly suspected2. A focus on transparency\, by contrast\, gives every one the same clear expectations and allows markers to assess what actually matters – whether the lea rner can explain the decisions they made\, the evi dence they relied on and the limits of their argum ent. In short\, it shifts the important questions from ‘who wrote this’ to ‘who is taking responsibi lity for the judgements in it\, and can they justi fy them?’\n \n \n \n 1. Weber-Wulff D\, Anohina-Na umeca A\, Bjelobaba S\, et al. Testing of detectio n tools for AI-generated text. Int J Educ Integr. 2023\;19(1):26.\n \n \n \n 2. Liang W\, Yuksekgonu l M\, Mao Y\, Wu E\, Zou J. GPT detectors are bias ed against non-native English writers. Patterns (N Y). 2023\;4(7):100779.\n \n \n \n Speaker bio\n \ n \n \n Dr Donna Johnson is the Course Director fo r Postgraduate Biomedical Sciences at Leeds Becket t University\, where she’s overseen MSc provision since 2015. She’s a Senior Fellow of the Higher Ed ucation Academy and a Chartered Science Teacher. M uch of her work centres on curriculum design that promotes student agency and supports learners from a range of academic and professional backgrounds. She’s especially interested in equipping postgrad uate students to think critically and work indepen dently\, while still offering structured\, inclusi ve support. Her teaching draws heavily on authenti c assessment and skills development\, particularly in communication\, applied research\, and data in terpretation. Alongside teaching\, she’s led a ser ies of research projects exploring learning innova tion in biomedical science.\n \n CATEGORIES:Conference CALSCALE:GREGORIAN DTSTAMP;TZID=Europe/London:20260630T130000 DTSTART;TZID=Europe/London:20260630T130000 DTEND;TZID=Europe/London:20260630T140000 LOCATION:Online event\, link will be provided\n United King dom\n URL:https://srhe.ac.uk/civicrm/event/info/?reset=1&id=804 END:VEVENT END:VCALENDAR