5.3.A Practical Use Cases
Estimated time: 20 minutes
Label: 5.3.A
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Practical Use Cases
Departmental Curriculum Lead Using AI for Live Signals
As Programme Director for Public Health, I use an AI assistant to run continuous environmental scanning around global health policy, digital epidemiology, and AI in assessment. Each Monday, I receive a brief summarising new WHO guidance, UK policy consultations, and emerging debates on AI-enabled surveillance. I ask the assistant to cluster these into trends affecting our MSc, linking directly to concepts from our environmental scanning and trend analysis lessons.
When a cluster of signals around “AI literacy for health professionals” appears, I prompt the AI to map these against our current modules and highlight gaps. It suggests options: a new elective on AI in population health, refreshed learning outcomes, and updated case studies. I then convene a small design group to review these AI-generated ideas, critique assumptions, and decide which to prototype for the next programme review cycle. Human academic judgement ultimately decides what is pedagogically sound and contextually appropriate.
To keep this work grounded, I also ask the AI to flag weak signals that might affect equity—for example, bias in AI triage tools used in lower-income settings—so we can embed global justice into curriculum redesign.
Follow-up AI prompts
“Scan UK and global public health bodies for new AI-in-health policies relevant to postgraduate teaching in the next 3–5 years.”
“Map emerging AI-in-health competencies against our current MSc modules and identify three curriculum gaps.”
“Generate scenario prompts exploring misuse of AI in public health decision-making for classroom debate.”
“Suggest inclusive case studies on AI and health from Global South contexts suitable for masters-level seminars.”
“Draft an agenda for a 90-minute curriculum meeting using AI-generated trend insights as discussion triggers.”
Faculty Digital Education Manager Stress-Testing a Digital Strategy
As Faculty Digital Education Manager, I am responsible for implementing a five-year digital learning strategy. Using lessons on simulations and stress-testing, I work with an AI assistant to model how our plan holds up under different futures. I first feed in core components of the strategy—AI-assisted marking pilots, virtual labs, and student analytics tools—alongside key external drivers from our trend analysis work.
The AI generates three contrasting stress-test scenarios: a data privacy backlash, a sudden funding cut, and a rapid policy shift mandating AI transparency. For each, it simulates likely impacts on staff workload, student trust, and infrastructure, using narrative and simple causal diagrams. I then ask the AI to propose mitigation options: phased roll-outs, explicit consent mechanisms, and diversified technology vendors. My role is to challenge these proposals, test them against local union agreements, and check alignment with institutional values. Human oversight is non-negotiable, especially where staff data, academic workload, and student rights are concerned.
I summarise the results into a “resilience map” for our faculty board, showing which parts of the strategy are robust across scenarios and which are fragile. This turns the AI simulations into concrete decisions: revising the implementation timetable, prioritising staff development, and earmarking contingency funds.
Follow-up AI prompts
“Simulate the impact of a major data breach on staff and student trust in our faculty’s digital strategy.”
“Stress-test our virtual lab roll-out under a 20% capital budget cut and propose three adaptation options.”
“Identify policy and regulatory trends that could constrain AI-assisted marking in UK higher education.”
“Draft a briefing for union representatives explaining our AI stress-testing approach and safeguards.”
“Propose staff development activities that would increase resilience across all three simulated futures.”
Cross-Institution Planner Using AI to Coordinate Scenarios
As Head of Strategic Planning across a multi-campus university, I use AI-supported scenario planning to align different local strategies. Each campus has distinct priorities—one research-intensive, one teaching-focused, one community-oriented—yet all are affected by sector-wide trends we explore in our scenario-building lessons.
I ask a generative AI assistant to synthesise campus-level planning documents, environmental scans, and trend analyses into three cross-institution strategic scenarios: “Fragmented Futures”, “Hybrid Excellence”, and “Local Anchor University”. For each, the AI describes plausible changes in student demographics, funding mixes, and AI adoption. I then convene workshops where campus teams critique these AI-generated narratives, adding local nuance and challenging unrealistic assumptions. AI gives us a fast, structured starting point; human stakeholders ensure stories remain grounded and inclusive.
Next, I use the AI to generate comparative matrices showing how each campus’s current plans perform within each scenario. We explore questions such as: which investments remain robust, where cross-campus collaboration would reduce risk, and what shared infrastructure (e.g., AI-augmented student support) is needed. Ethical oversight is built in through a small governance group that reviews AI outputs for bias, sector blind spots, and unintended consequences for disadvantaged students.
Follow-up AI prompts
“Generate three contrasting institution-wide scenarios for our multi-campus university based on our current environmental scans.”
“Create a comparison table of risks and opportunities for each campus under the ‘Hybrid Excellence’ scenario.”
“Suggest workshop activities that help staff interrogate AI-generated scenarios rather than accept them uncritically.”
“Identify shared AI infrastructure investments that would be beneficial across all campuses and scenarios.”
“Draft a communication plan explaining scenario-planning outcomes to staff and students in accessible language.”
National Policy Officer Using AI for Sector Foresight
As a Senior Policy Officer in a UK sector body, I am tasked with advising on long-term funding and regulation. I use generative AI to perform national-level trend analysis and foresight, building directly on the methods described in the trend analysis and generative foresight lessons.
I start by asking the AI to synthesise data from government consultations, HESA statistics, and international reports into coherent trend clusters: AI in assessment, modular provision, international student volatility, and widening participation. Next, I prompt the AI to generate national-level scenarios describing how these clusters might evolve over 10–15 years, focusing particularly on funding models and regulatory oversight.
These AI-generated insights form a first draft of sector scenarios, which I then refine with colleagues, institutional representatives, and student unions. We interrogate who benefits, who bears the risk, and what assumptions the AI may have baked in about “typical” universities. Human deliberation and values-based scrutiny are central to deciding which futures are desirable, plausible, or to be avoided.
From this work, I ask the AI to propose policy levers—pilot funds, regulatory sandboxes, or capability schemes—that would help institutions stress-test their strategies and link foresight to operational practice. These ideas are translated into options papers for ministers, with clear explanation of how AI analysis was used and where human judgement overrode automated suggestions.
Follow-up AI prompts
“Summarise the top ten national trends in UK higher education over the next decade using public data sources.”
“Generate three plausible national funding futures and their impact on diverse institutional types.”
“Identify risks of reinforcing inequality in AI-driven sector forecasts and propose mitigation strategies.”
“Draft consultation questions that invite universities to respond to AI-generated sector scenarios.”
“Produce a briefing note explaining how AI-assisted foresight informed our recommended policy options.”
Interdisciplinary Institute Director Using AI Simulations for Operations
As Director of an AI–Climate Futures Institute, I sit at the intersection of disciplines and missions. Our work spans research, postgraduate education, and policy engagement. To connect strategic foresight with daily operations, I use generative AI to simulate how different climate and AI policy futures affect our institute’s portfolio.
Drawing on lessons about stress-testing and linking foresight to operational strategy, I first ask the AI to integrate global climate scenarios, AI governance debates, and UK research funding trends into a set of interdisciplinary futures. For each, the AI models how demand might shift for our programmes, what types of partnerships would matter, and where research impact opportunities emerge.
I then use AI to translate these futures into operational questions: which programmes to scale, which new microcredentials to prototype, and how to sequence hiring across disciplines. The assistant recommends KPIs—such as diversity of partner sectors, proportion of projects using responsible AI tools, or resilience of our funding mix. My leadership team reviews these proposals, adjusting metrics to reflect our values on equity, openness, and community impact. AI offers integrative pattern recognition; human governance ensures ethical and strategic coherence.
This loop runs twice a year, with AI updating simulations based on new environmental scanning data. It turns scenario planning into an ongoing, interdisciplinary practice rather than a one-off strategy exercise.
Follow-up AI prompts
“Combine IPCC climate scenarios and AI governance debates into three futures relevant to an AI–Climate Futures Institute.”
“Simulate how our taught programmes and research themes perform under each future and highlight gaps.”
“Propose foresight-informed KPIs that connect our strategic themes to operational delivery and partnerships.”
“Generate partnership archetypes (industry, NGO, city government) appropriate to each future scenario.”
“Draft a short foresight-informed annual planning brief for institute staff, linking scenarios to next year’s priorities.”
Framework alignment
This lesson sits within: CloudPedagogy AI Capability Framework (2026 Edition)
Domains: Awareness, Co-Agency, Applied Practice & Innovation, Ethics, Equity & Impact, Decision-Making & Governance, Reflection, Learning & Renewal
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