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Panellists will discuss emerging pedagogical opportunities, challenges and the implications for staff, students and institutions. The session will be followed by an audience Q&A.) /Pg 9 0 R /K [10] >> endobj 46 0 obj << /Type /StructElem /S /P /P 31 0 R /E (GenAI and the Pedagogical Power of Recognition in Feedback) /Pg 9 0 R /K [11] >> endobj 47 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Dr Thomas Corbin, Deakin University, Australia) /Pg 9 0 R /K [12] >> endobj 48 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Generative Artificial Intelligence \(GenAI\) systems seem to demonstrate impressive capabilities in providing various forms of feedback. However, in this talk I will mount the case that claims to its potential overlook a fundamental aspect of effective feedback between humans: recognition between teacher and student. This paper critically examines the role of GenAI in providing feedback within higher education contexts, drawing on both the established feedback literature and philosophical work on recognition. Effective feedback is predicated on trusting and respectful relationships, which are in turn grounded in mutual recognition of shared vulnerability and agency. GenAI systems, lacking the capacity for genuine recognition, operate outside of this relational framework. Therefore, while valuable, GenAI feedback cannot fully replicate the pedagogical efficacy of human-provided feedback. However, the limitations of GenAI feedback may at the same time offer unique pedagogical opportunities. ) /Pg 9 0 R /K [13] >> endobj 49 0 obj << /Type /StructElem /S /P /P 31 0 R /E <556E6465727374616E64696E6720556E69766572736974792053747564656E74739020456E676167656D656E7420776974682047656E6572617469766520414920466565646261636B> /Pg 9 0 R /K [14] >> endobj 50 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Dr Ying \(Jane\) Zhan, The Education University of Hong Kong, Hong Kong) /Pg 9 0 R /K [15] >> endobj 51 0 obj << /Type /StructElem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g 9 0 R /K [16] >> endobj 52 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Generative AI and the Future of Feedback: What We Must Get Right) /Pg 9 0 R /K [17] >> endobj 53 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Dr Seyyed Kazem Banihashem, Maastricht University, the Netherlands) /Pg 9 0 R /K [18] >> endobj 54 0 obj << /Type /StructElem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g 9 0 R /K [19] >> endobj 55 0 obj << /Type /StructElem /S /P /P 31 0 R /E (The Care-full Craft of Feedback in an Age of Generative AI) /Pg 9 0 R /K [20] >> endobj 56 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Professor Naomi Winstone, University of Surrey, UK) /Pg 9 0 R /K [21] >> endobj 57 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Generative AI is rapidly reshaping how students and educators engage in feedback encounters. But how is GenAI prompting us to rethink not only what effective feedback looks like, but what we value in meaningful learning? Drawing on our recent Manifesto for Feedback in the Age of GenAI, I consider how GenAI can be woven into a feedback ecosystem grounded in care, trust, and professional craft. I position feedback as a relational practice where multiple modes of feedback can enrich learning. I also consider some emerging directions for designing thoughtful, care-full feedback processes in an AI-enabled future. ) /Pg 9 0 R /K [22 195 0 R 24] >> endobj 58 0 obj << /Type /StructElem /S /Span /K [23] /Pg 9 0 R /P 195 0 R >> endobj 59 0 obj << /Type /StructElem /S /H2 /P 31 0 R /T (Event Programme ) /E (Event Programme ) /Pg 9 0 R /K [25] >> endobj 60 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Chair: Dr Tiffany Chiu, ) /Pg 9 0 R /K [26] >> endobj 61 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Queens Tower Rooms) /Pg 9 0 R /K [27] >> endobj 62 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Huxley LT Clore) /Pg 9 0 R /K [28] >> endobj 63 0 obj << /Type /StructElem /S /P /P 31 0 R /E (Huxley LT Clore) /Pg 9 0 R /K [29] >> endobj 64 0 obj << /Type /StructElem /S /Figure /P 31 0 R /Pg 9 0 R /A [286 0 R 0] /K [30] >> endobj 65 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 11.30 - 12.00 Parallel Session 1: Spotlight Internal Perspectives) /Pg 10 0 R /K [0] >> endobj 66 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 1.1 Advancing Lab-Based Guidance Using an AI-Driven Virtual Assistant) /Pg 10 0 R /K [1] >> endobj 67 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( Dr Mohamed Maher, ) /Pg 10 0 R /K [2] >> endobj 68 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( This presentation shares findings from a project exploring how an AI-driven virtual assistant can enhance guidance in laboratory-based teaching environments. The ) /Pg 10 0 R /K [3] >> endobj 69 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( initiative aimed to provide students with timely, context-aware support during practical sessions, reducing reliance on immediate instructor availability and improving ) /Pg 10 0 R /K [4] >> endobj 70 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( learning continuity. We discuss the design and deployment of the assistant, its integration with existing lab workflows, and the impact on student engagement and) /Pg 10 0 R /K [5] >> endobj 71 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( autonomy. Key outcomes, challenges, and future directions are presented, highlighting how AI can complement human instruction to create more responsive and ) /Pg 10 0 R /K [6] >> endobj 72 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( scalable lab-based learning experiences.) /Pg 10 0 R /K [7] >> endobj 73 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 1.2 Transforming GTA Feedback Consistency and Usefulness with LLM: Improving Clarity, Consistency, and Actionability) /Pg 10 0 R /K [8] >> endobj 74 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( Dr Zohaib Akhtar, ) /Pg 10 0 R /K [9] >> endobj 75 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( This presentation addresses a key challenge in higher education: ensuring students receive consistent, clear, and pedagogically useful feedback, particularly in large ) /Pg 10 0 R /K [10] >> endobj 76 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( cohorts assessed by multiple Graduate Teaching Assistants \(GTAs\). Feedback is provided for over 200 students in lab oral assessments, making it difficult to ensure ) /Pg 10 0 R /K [11] >> endobj 77 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( quality and uniformity across the marking cohort, while moderation currently only takes into account final marks rather than quality and consistency of the written ) /Pg 10 0 R /K [12] >> endobj 78 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( feedback. We will present our recent initiative to employ a Large Language Model \(LLM\)-powered tool to enhance the clarity, consistency, and actionability of GTA-) /Pg 10 0 R /K [13] >> endobj 79 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( generated feedback. The tool improves tone, align comments with rubrics, and promote feedforward and reflection.) /Pg 10 0 R /K [14] >> endobj 80 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 1.3 AI-Supported Feedback: A Figma Plugin to Enhance Decision Making in Team Project Based Learning) /Pg 10 0 R /K [15] >> endobj 81 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( Dr Stephen Green, Katherine Luo, ) /Pg 10 0 R /K [16] >> endobj 82 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( This session introduces a prototype Figma-based AI feedback tool designed to support students working in team project-based learning environments. The plugin ) /Pg 10 0 R /K [17] >> endobj 83 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( provides structured, context-aware guidance to help teams analyse design decisions, reflect on their process, and improve the quality of collaborative outcomes.) /Pg 10 0 R /K [18] >> endobj 84 0 obj << /Type /StructElem /S /H3 /P 32 0 R /T (11.15 - 11.30 Refreshment break) /E (11.15 - 11.30 Refreshment break) /Pg 10 0 R /K [19] >> endobj 85 0 obj << /Type /StructElem /S /H2 /P 32 0 R /T (Event Programme ) /E (Event Programme ) /Pg 10 0 R /K [20] >> endobj 86 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 12.05 - 12.35 Parallel Session 2: Spotlight Internal Perspectives) /Pg 10 0 R /K [21] >> endobj 87 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 2.1 Feedback and Learning: AI Assisted & Reimagined Project \(FLAIR\)) /Pg 10 0 R /K [22] >> endobj 88 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( Dr Tiffany Chiu, Dr Monika Pazio Rossiter, Dr Ian Radcliffe, Dr Nashwa Ismail, Imperia) /Pg 10 0 R /K [23] >> endobj 89 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( This session introduces the university-wide Feedback and Learning: AI-Assisted & Reimagined \(FLAIR\) project, which explores how GenAI can enhance feedback ) /Pg 10 0 R /K [24 196 0 R 26] >> endobj 90 0 obj << /Type /StructElem /S /Span /K [25] /Pg 10 0 R /P 196 0 R >> endobj 91 0 obj << /Type /StructElem /S /P /P 32 0 R /E <202020202020202020202020202020202020202020202020202020202020202020202020202070726163746963657320696E2068696768657220656475636174696F6E2E2044726177696E67206F6E2063726F73732D666163756C74792065766964656E63652066726F6D2073747564656E7420666F6375732067726F757020646174612C2077652077696C6C207368617265207468652070726F6A65637490732070726F677265737320616E64206B6579207072656C696D696E61727920> /Pg 10 0 R /K [27] >> endobj 92 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( findings, highlighting significant challenges and opportunities, and reflecting on effective human-AI feedback practices.) /Pg 10 0 R /K [28] >> endobj 93 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( 2.2 Enhancing Assessment Feedback with GenAI: Towards Consistency, Clarity and Timely Support) /Pg 10 0 R /K [29] >> endobj 94 0 obj << /Type /StructElem /S /P /P 32 0 R /E <202020202020202020202020202020202020202020202020202020202020202020202020202044722050657465722041746B696E736F6E2C205365616E204F9047726164792C205374657068656E2056617A2C20496D70657269616C20436F6C6C656765204C6F6E646F6E> /Pg 10 0 R /K [30] >> endobj 95 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( ImperiaBusiness School is developing an early-stage prototype GenAI tool designed to support academics in delivering high-quality feedback on formative and ) /Pg 10 0 R /K [31] >> endobj 96 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( summative assessments. The project aims to improve the consistency, clarity and timeliness of feedback while reducing workload and mitigating potential bias. The ) /Pg 10 0 R /K [32] >> endobj 97 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( tool acts as an assistive layer, helping academics generate structured, actionable comments aligned with module-level criteria and effective feedback principles. ) /Pg 10 0 R /K [33] >> endobj 98 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( This work builds on our wider initiatives exploring AI Tutors and academic Digital Twins as part of a broader ecosystem of GenAI-enabled student support. In this ) /Pg 10 0 R /K [34] >> endobj 99 0 obj << /Type /StructElem /S /P /P 32 0 R /E ( session, we will share our approach and emerging insights to date.) /Pg 10 0 R /K [35] >> endobj 100 0 obj << /Type /StructElem /S /Figure /P 32 0 R /Pg 10 0 R /A [287 0 R 0] /K [36] >> endobj 101 0 obj << /Type /StructElem /S /H1 /P 32 0 R /T (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /E (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /Pg 10 0 R /K [37] >> endobj 102 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Queens Tower Rooms) /Pg 10 0 R /K [38] >> endobj 103 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Queens Tower Rooms) /Pg 10 0 R /K [39] >> endobj 104 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Huxley 342) /Pg 10 0 R /K [40] >> endobj 105 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Huxley LT Clore) /Pg 10 0 R /K [41] >> endobj 106 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Huxley LT Clore) /Pg 10 0 R /K [42] >> endobj 107 0 obj << /Type /StructElem /S /P /P 32 0 R /E (Queens Tower Rooms) /Pg 10 0 R /K [43] >> endobj 108 0 obj << /Type /StructElem /S /Figure /P 32 0 R /Pg 10 0 R /A [288 0 R 0] /K [44] >> endobj 109 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 13.30 - 14.15 Parallel session 3: Spotlight External Perspectives) /Pg 11 0 R /K [0] >> endobj 110 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 3.1 From Evaluation to Evolution: Unlocking the Power of Feedback Mining with GenAI) /Pg 11 0 R /K [1] >> endobj 111 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Dr Mustafa Akben, Elon University, USA) /Pg 11 0 R /K [2] >> endobj 112 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( In higher education, student feedback is traditionally a one-way flow of information from instructors to learners. In this presentation, Dr. Mustafa Akben will argue that ) /Pg 11 0 R /K [3] >> endobj 113 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( this process should be bi-directional. Instructors might consider using generative AI to analyze the feedback they receive at a scale. Dr. Akben will introduce the idea of ) /Pg 11 0 R /K [4] >> endobj 114 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( feedback mining, which refers to using generative AI to process student feedback holistically and efficiently. He will also illustrate how unstructured feedback data can be ) /Pg 11 0 R /K [5] >> endobj 115 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( used to generate actionable insights for teaching. He, finally, will encourage attendees to rethink the feedback loop by recognizing how generative AI can create ) /Pg 11 0 R /K [6] >> endobj 116 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( opportunities to continuously refine and personalize teaching strategies and improve student learning outcomes.) /Pg 11 0 R /K [7] >> endobj 117 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 3.2 Everything Flows: Locating Generative AI and Human Capabilities as Third Spaces for Teaching, Learning and Assessment in ) /Pg 11 0 R /K [8] >> endobj 118 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Uncertain Times ) /Pg 11 0 R /K [9] >> endobj 119 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Professor Peter Bryant, University of Sydney, Australia) /Pg 11 0 R /K [10] >> endobj 120 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Generative AI, as a conceptual, policy, and strategic influence on higher education, has accelerated the complexity of assessment and feedback design and the ) /Pg 11 0 R /K [11] >> endobj 121 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( technological reactivity of the architectures underpinning how and what we teach. It disrupts policy, integrity, curriculum, student experience, and employability. It ) /Pg 11 0 R /K [12] >> endobj 122 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( dominates strategic discourse, often driven by hype and vendor influence, sidelining core values, and disempowering those caught in liminal spaces of uncertainty. ) /Pg 11 0 R /K [13] >> endobj 123 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Engagement with AI is rarely a safe space for academics, practitioners, or students, yet its use is mandated and shaped by industry, regulators, vendors, and leadership. ) /Pg 11 0 R /K [14] >> endobj 124 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Yet there are also responsible, critical, and innovative ways to engage with AI in higher education. This presentation will highlight some of the most urgent and thought-) /Pg 11 0 R /K [15] >> endobj 125 0 obj << /Type /StructElem /S /P /P 33 0 R /E <202020202020202020202020202020202020202020202020202020202020202020202020202070726F766F6B696E6720646562617465732061726F756E642041492E2049742077696C6C206578706C6F726520746865208D7468697264207370616365738E207468617420656D65726765207768656E20696E737469747574696F6E7320616E6420696E646976696475616C732074616B6520706F736974696F6E732077697468696E207468657365206465626174657320616E64206F666665722070726163746963616C20> /Pg 11 0 R /K [16] >> endobj 126 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( examples of how they are playing out across the sector. Ultimately, this provocative and thought-provoking talk will seek to make sense of the current moment of crisis ) /Pg 11 0 R /K [17] >> endobj 127 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( while pointing to the transformative potential of higher education to invent, ideate, and inspire the new and the novel.) /Pg 11 0 R /K [18] >> endobj 128 0 obj << /Type /StructElem /S /P /P 33 0 R /E (12.35 - 13.30 Lunch break) /Pg 11 0 R /K [19] >> endobj 129 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Event Programme ) /Pg 11 0 R /K [20] >> endobj 130 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 14.20 - 15.20 Parallel Session 4: Interactive Workshop) /Pg 11 0 R /K [21] >> endobj 131 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 4.1 Emotional Intelligence Meets Artificial Intelligence: Exploring the Use of AI as Emotional Mediator) /Pg 11 0 R /K [22] >> endobj 132 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Dr Monika Pazio Rossiter, Dr Kate Ippolito, Imperia) /Pg 11 0 R /K [23] >> endobj 133 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Recent scholarship highlights the affective dimensions of feedback and the need for both students and staff to manage emotions effectively when engaging with ) /Pg 11 0 R /K [24] >> endobj 134 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( feedback. Emotional intelligence \(EI\), the ability to anticipate and respond to emotional impact, is central to this process, but it is not innate and must be developed. This ) /Pg 11 0 R /K [25] >> endobj 135 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( workshop explores both how the differing impact of human feedback and AI feedback on learner and teacher emotions can be leveraged, and how AI can support the ) /Pg 11 0 R /K [26] >> endobj 136 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( development of EI, moving beyond automated feedback systems toward using AI as a mediator to enhance emotional understanding. Through interactive activities, ) /Pg 11 0 R /K [27] >> endobj 137 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( participants will reflect on the role of emotions in feedback, compare the strengths and limitations of human feedback and AI feedback with a focus on affective impact, ) /Pg 11 0 R /K [28] >> endobj 138 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( and critically consider the opportunities and challenges of these approaches.) /Pg 11 0 R /K [29] >> endobj 139 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 4.2 Show & Tell: Innovations in Feedback Practice) /Pg 11 0 R /K [30] >> endobj 140 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Chair: Dr Nashwa Ismail, ) /Pg 11 0 R /K [31] >> endobj 141 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( 4.2.1 Generative AI for Automated Feedback on Software Design ) /Pg 11 0 R /K [32] >> endobj 142 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( Dr Robert Chatley, ) /Pg 11 0 R /K [33] >> endobj 143 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( This talk will share our experience using LLMs to provide automated feedback on student coding and software design exercises. Rather than testing functional correctness ) /Pg 11 0 R /K [34] >> endobj 144 0 obj << /Type /StructElem /S /P /P 33 0 R /E ( with pre-written unit tests, we want to give feedback on how the code is designed, not the output that it produces. Doing this on a regular basis at scale requires a large ) /Pg 11 0 R /K [35] >> endobj 145 0 obj << /Type /StructElem /S /Figure /P 33 0 R /Pg 11 0 R /A [289 0 R 0] /K [36] >> endobj 146 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /Pg 11 0 R /K [37] >> endobj 147 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Huxley LT Clore) /Pg 11 0 R /K [38] >> endobj 148 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Huxley 342) /Pg 11 0 R /K [39] >> endobj 149 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Huxley 342) /Pg 11 0 R /K [40] >> endobj 150 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Queens Tower Rooms) /Pg 11 0 R /K [41] >> endobj 151 0 obj << /Type /StructElem /S /P /P 33 0 R /E (Queens Tower Rooms) /Pg 11 0 R /K [42] >> endobj 152 0 obj << /Type /StructElem /S /Figure /P 33 0 R /Pg 11 0 R /A [290 0 R 0] /K [43] >> endobj 153 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( human effort. Using the latest LLM models, we can now generate rich, personalised feedback for hundreds of students much more quickly, supporting ) /Pg 12 0 R /K [0] >> endobj 154 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( consistent and timely formative assessment at scale. I will discuss examples from a recent undergraduate software engineering course and reflect on ) /Pg 12 0 R /K [1] >> endobj 155 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( lessons learned about methodology, effectiveness, and student perceptions.) /Pg 12 0 R /K [2] >> endobj 156 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( 4.2.2 Using Generative AI to Give Feedback on Formal Proofs ) /Pg 12 0 R /K [3] >> endobj 157 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Dr Marie-Amelie Lawn, ) /Pg 12 0 R /K [4] >> endobj 158 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Can large language models improve feedback on undergraduate proof writing without lowering standards? I present a practical way to guide an LLM so its comments are ) /Pg 12 0 R /K [5] >> endobj 159 0 obj << /Type /StructElem /S /P /P 34 0 R /E <2020202020202020202020202020202020202020202020202020202020202020202020202020666F63757365642C20636F6E73697374656E742C20616E6420616C69676E6564207769746820636F757273652061696D73847768696C652061636164656D6963206A756467656D656E7420737461797320776974682074686520696E7374727563746F722E20496E20612070696C6F7420776974682066697273742D7965617220776F726B2C207468697320617070726F61636820737572666163656420636F6D6D6F6E20> /Pg 12 0 R /K [6] >> endobj 160 0 obj << /Type /StructElem /S /P /P 34 0 R /E <20202020202020202020202020202020202020202020202020202020202020202020202020206D6973636F6E63657074696F6E73206561726C7920616E642073686F7274656E656420666565646261636B206379636C65732E2049906C6C206F75746C696E652074686520696465612C206E6F746520776861742068656C70656420616E642077686174206661696C65642C20616E6420646973637573732077686572652041492063616E20726573706F6E7369626C792073697420616C6F6E67736964652068756D616E2020> /Pg 12 0 R /K [7] >> endobj 161 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( marking.) /Pg 12 0 R /K [8] >> endobj 162 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( 4.2.3 Lambda Feedback - Timely, Automated Feedback on Self-Study ) /Pg 12 0 R /K [9] >> endobj 163 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Dr Peter Johnson, ) /Pg 12 0 R /K [10] >> endobj 164 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Timely formative feedback on self-study is valuable but practically challenging. Automation of task-level feedback, such as on answers to exercises, can meet this need. ) /Pg 12 0 R /K [11] >> endobj 165 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( This workshop will introduce you to Lambda Feedback, a self-study platform that provides automated feedback, including on essays and handwritten mathematics. ) /Pg 12 0 R /K [12] >> endobj 166 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Lambda Feedback was developed at Imperiaover the past four years. Its use across the University includes over 60 modules, 10,000+ questions, and over 1 million ) /Pg 12 0 R /K [13] >> endobj 167 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( feedback events.) /Pg 12 0 R /K [14] >> endobj 168 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( 4.2.4 Designing Pedagogical Frameworks for Effective AI-Assisted Feedback) /Pg 12 0 R /K [15] >> endobj 169 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Dr Rhodri Nelson, ) /Pg 12 0 R /K [16] >> endobj 170 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Large language models \(LLMs\) are increasingly used to support assessment and feedback, but their effectiveness depends less on the model itself and more on the ) /Pg 12 0 R /K [17] >> endobj 171 0 obj << /Type /StructElem /S /P /P 34 0 R /E <202020202020202020202020202020202020202020202020202020202020202020202020202070656461676F676963616C206672616D65776F726B20777261707065642061726F756E642069742E20496E20746869732074616C6B2C2049206672616D6520746865204C4C4D2061732061208D636C65766572204754418E3A2062726F61646C79206B6E6F776C6564676561626C6520616E64206C696E67756973746963616C6C7920736F70686973746963617465642C20627574206C61636B696E6720746865206C6F63616C2C20> /Pg 12 0 R /K [18] >> endobj 172 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( tacit understanding of how you teach the module and what counts as good performance. I will argue that effective AI-assisted feedback requires designed context \(not ) /Pg 12 0 R /K [19] >> endobj 173 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( incidental context\), explicit learning outcomes, carefully structured rubrics, and prompt workflows that decompose marking into clear, answerable questions. By encoding ) /Pg 12 0 R /K [20] >> endobj 174 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( assessment criteria in a machine-readable yet pedagogically meaningful way, and by orchestrating prompts that progressively build and refine feedback, we can align the ) /Pg 12 0 R /K [21] >> endobj 175 0 obj << /Type /StructElem /S /P /P 34 0 R /E <20202020202020202020202020202020202020202020202020202020202020202020202020206D6F64656C9073206A7564676D656E74732077697468206F757220646F6D61696E2D7370656369666963206578706563746174696F6E732E2044726177696E67206F6E206C697665206465706C6F796D656E747320696E204D5363207465616368696E672C20492077696C6C2073686F7720686F772073756368206672616D65776F726B732063616E2070726F6475636520666565646261636B207468617420697320> /Pg 12 0 R /K [22] >> endobj 176 0 obj << /Type /StructElem /S /P /P 34 0 R /E <20202020202020202020202020202020202020202020202020202020202020202020202020206D6F726520636F6E73697374656E742C207472616E73706172656E742C20616E6420656475636174696F6E616C6C792075736566756C207468616E20616420686F632070726F6D7074696E672C207768696C65206B656570696E672061636164656D6963207374616666206669726D6C79208D696E20746865206C6F6F708E206173207468652066696E616C206172626974657273206F66207175616C6974792E> /Pg 12 0 R /K [23] >> endobj 177 0 obj << /Type /StructElem /S /H2 /P 34 0 R /T (Event Programme ) /E (Event Programme ) /Pg 12 0 R /K [24] >> endobj 178 0 obj << /Type /StructElem /S /Figure /P 34 0 R /Pg 12 0 R /A [291 0 R 0] /K [25] >> endobj 179 0 obj << /Type /StructElem /S /H1 /P 34 0 R /T (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /E (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /Pg 12 0 R /K [26] >> endobj 180 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( 15.35 - 16.50 Student-Staff Panel Discussion, followed by a Q&A from the audience) /Pg 12 0 R /K [27] >> endobj 181 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Chair: Dr Monika Pazio Rossiter, ) /Pg 12 0 R /K [28] >> endobj 182 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Panellists: Emina Hogas, Student Union; Sam Ahadi, Department of Physics; Emma Blyth, Faculty of Medicine; Dr Tim Kimber, Department of Computing; ) /Pg 12 0 R /K [29] >> endobj 183 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( Parastoo Salah, Department of Earth Science Engineering; Claire Aurelia Shyle, Department of Life Sciences) /Pg 12 0 R /K [30] >> endobj 184 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( This session brings together students and staff for a panel discussion on how feedback practices are evolving in the age of Generative AI. Panellists will share their ) /Pg 12 0 R /K [31] >> endobj 185 0 obj << /Type /StructElem /S /P /P 34 0 R /E <2020202020202020202020202020202020202020202020202020202020202020202020202020657870657269656E636573206F66207768617420776F726B732C207768617420646F65736E907420616E6420686F772047656E414920697320726573686170696E6720666565646261636B2070726F636573736573206163726F7373206469736369706C696E65732E2054686520636F6E766572736174696F6E2077696C6C20686967686C69676874207065727370656374697665732C2070726163746963616C20> /Pg 12 0 R /K [32] >> endobj 186 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( examples and emerging challenges, offering valuable insights for staff involved in teaching, learning, or educational development. The panel discussion will be followed ) /Pg 12 0 R /K [33] >> endobj 187 0 obj << /Type /StructElem /S /P /P 34 0 R /E ( by a live Q&A, giving the audience an opportunity to contribute reflections, ask questions and engage directly with the speakers) /Pg 12 0 R /K [34] >> endobj 188 0 obj << /Type /StructElem /S /H3 /P 34 0 R /T (15.20 - 15.35 Refreshment break) /E (15.20 - 15.35 Refreshment break) /Pg 12 0 R /K [35] >> endobj 189 0 obj << /Type /StructElem /S /H3 /P 34 0 R /T (16.50 - 17.00 Closing remarks ) /E (16.50 - 17.00 Closing remarks ) /Pg 12 0 R /K [36] >> endobj 190 0 obj << /Type /StructElem /S /P /P 34 0 R /E (Professor Sophie Rutschmann \(Associate Provost, Digitally-Enhanced Learning & Teaching\)) /Pg 12 0 R /K [37] >> endobj 191 0 obj << /Type /StructElem /S /P /P 34 0 R /E (Queens Tower Rooms) /Pg 12 0 R /K [38] >> endobj 192 0 obj << /Type /StructElem /S /P /P 34 0 R /E (Huxley LT Clore) /Pg 12 0 R /K [39] >> endobj 193 0 obj << /Type /StructElem /S /P /P 34 0 R /E (Huxley LT Clore) /Pg 12 0 R /K [40] >> endobj 194 0 obj << /Type /StructElem /S /Figure /P 34 0 R /Pg 12 0 R /A [292 0 R 0] /K [41] >> endobj 195 0 obj << /Type /StructElem /S /Link /P 57 0 R /Alt (Manifesto for Feedback in the Age of GenAI) /Pg 9 0 R /K [58 0 R 293 0 R] >> endobj 196 0 obj << /Type /StructElem /S /Link /P 89 0 R /Alt (Feedback and Learning: AI-Assisted & Reimagined \(FLAIR\) project) /Pg 10 0 R /K [90 0 R 294 0 R] >> endobj 197 0 obj << /Title (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /Dest [9 0 R /Fit] /Parent 7 0 R /Prev 15 0 R /First 295 0 R /Last 296 0 R /Count -3 /Next 198 0 R >> endobj 198 0 obj << /Title (Advancing Feedback Practice with GenAI: Pedagogical Challenges and Opportunities) /Dest [10 0 R /Fit] /Parent 7 0 R /Prev 197 0 R /First 297 0 R /Last 298 0 R /Count -2 /Next 16 0 R >> endobj 199 0 obj << /Title (15.20 - 15.35 Refreshment break) /Dest [12 0 R /Fit] /Parent 16 0 R /Next 200 0 R >> endobj 200 0 obj << /Title (16.50 - 17.00 Closing remarks) /Dest [12 0 R /Fit] /Parent 16 0 R /Prev 199 0 R >> endobj 201 0 obj << /ca 1 /BM /Normal >> endobj 202 0 obj << /Type /ExtGState /ca 0.0 >> endobj 203 0 obj << /G3 201 0 R /G5 299 0 R /G83 300 0 R >> endobj 204 0 obj << /Length 218 /Type /XObject /Subtype /Form /Resources << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /ExtGState 301 0 R /XObject << /X4 302 0 R >> >> /BBox [0 0 4374 2692] /Group 303 0 R /Filter /FlateDecode >> stream xmN1 D 7>BA=JP\Lf2j9^w V!iZ= 7&mX3nw T T4taIJXa1ҚՁ)Sn5c'A=5 5k=C3gdD}(4O_^Ox:۟aôe]ezXw hE endstream endobj 205 0 obj << /Length 219 /Type /XObject /Subtype /Form /Resources << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /ExtGState 304 0 R /XObject << /X7 305 0 R >> >> /BBox [0 0 2713 150] /Group 306 0 R /Filter /FlateDecode >> stream xmN1 D 7v>BA=^"(H /TTh| NxeTXf1B#T J> >> /BBox [0 0 1993 86] /Group 309 0 R /Filter /FlateDecode >> stream xeRN1 +qbz \ RvmҢFI2vN%;eA5Rpl0+zV$lggȁ&GaT 8dt"VcpB 1`AQL Α͏& 2L1IQuNSN* S#{q"  ["7lh4pSN\:An~'h霷 h蹴6i_l+kq!`7qVUgC endstream endobj 207 0 obj << /Length 112 /Type /XObject /Subtype /Form /Resources << /ProcSet [/PDF /Text /ImageB /ImageC /ImageI] /ExtGState 310 0 R >> /BBox [0 0 842 3507] /Group 311 0 R /Filter /FlateDecode >> stream xm;PF~Voa^APQ baXX9o VJ@TشXS:+^^%p`ۨN8#}72j t< endstream endobj 208 0 obj << /Length 27185 /Type /XObject /Subtype /Image /Width 400 /Height 400 /ColorSpace [/ICCBased 312 0 R] /SMask 313 0 R /BitsPerComponent 8 /Filter /FlateDecode >> stream xS[Yy 0HZX-o"vzK;ofz/ 0^2*KUU]]"zޘ~xg&&:#&9Wig٬O<垫{zις#D#d\!Fr` >V!n 1$&F>C`cB!$lݴ;ykmeh4*hXrBm $KTzMQfJvJGfauz6J\o\ #M!Ҫ4'T=}!&]EO9 uBk 'UiūwT YV+̚S%Ƌ91Ɂ@^r 5:Y۴=Y{~= aUʤr5MT&[?CNW(>gj$T(>4՜ٖ/֞umɦY`eDٺټ-Df֞RNfDV6WbMrŚ}ri+l,pՖ- ԟ:g՘Y_܄H}UVb{M?Irʭ [>Hy]͑ Z5'ˍ4\pޔ}T[.7(6 6姤lG )p"mҥ*EY;ozJE(F@B*ڶ"!7SQ]57ukSzb~v!(S$n@RgͽbFg^GrESa@ɉ%ǠVY+s/!6ѧ<2hR2AWk3j6rϔZiSaku:m/B sS3,y7CT+r?jHKi O1uJ'BⶤS 2Jno*S6}ocSGy ;nXMMƋ Ϸ:y7 yه vmz5cc?/`e%]c[)zu.XcS[zu y۩uESΗe8/hAq;s2PX\b:U}RnK%ˈ z-sz*2 s^ҫ샯{BAyA[ JtWii:hR5jhJNM-[:ȿ肓!:CݔuZ*MWKtm&6219ƎZWzzPtIm{,:TE[!0kM"zg >- !@Xt-e}~Uj!FOE @x;B) |Ǵ͢k8qs6颲TV׻~(VʶsOLܜQ93ǢsU 9䥢?܁G׃FLj1+R>Vi\kJ85Q|Лw0s%kNlWSRC{DzJ~4Bg^:Lػmk~x%kNݡEh~ zsWQunZZi}|t\*%Et?LjI)9Va6(v&ʁ?f=[iIe賏278+ްLšqfJZ۔ݔ4?_Fր,JJ*R&W;,T !#T++%BBjb6-՟uN,V H(@)h.0h¤4T8Dj5k%o-& *ykyZѬm)gvUuB; 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