5.3.B Learning Activities
Estimated time: 30 minutes
Label: 5.3.B
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Learning objectives
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Learning Activities
Designing a Live Environmental Scanning Briefing (Director of Strategy)
Objective
To design and trial an AI-enabled environmental scanning and briefing process that supports real-time strategic awareness for senior leadership.
Task
You are the Director of Strategy at a UK university preparing a monthly “Strategic Signals Briefing” for the executive team. Using a generative AI assistant, you will design and test a workflow that transforms dispersed data streams into an integrated, foresight-oriented briefing.
First, identify three scanning domains that matter most to your institution over the next three years. For example: policy and regulation, digital and AI in education, and student demand and demographics. For each domain, assemble a short list of representative sources (such as sector bodies, government portals, think tanks, edtech reports, labour market analyses).
Next, use the AI assistant to generate a structured summary for each domain, including: top three developments in the last month, weak signals worth watching, and potential implications for teaching, research, or operations. Then prompt the AI to synthesise across domains, asking it to identify cross-cutting themes, contradictions, and possible “noisy but irrelevant” signals.
Finally, work with the AI to produce a concise, two-page Strategic Signals Briefing designed for non-specialist senior leaders. Include a one-paragraph narrative overview, three strategic questions for discussion, and a short list of suggested next steps or areas for deeper investigation.
Example Prompt
“You are acting as a strategic foresight analyst for a UK university. Using UK and international higher education sources, generate a draft ‘Strategic Signals Briefing’ for the last 30 days. Focus on three domains: policy and regulation, digital and AI in education, and student demand/demographics. For each domain, summarise three key developments, two weak signals, and likely implications for teaching, research, and institutional risk. Then write a one-page synthesis highlighting cross-cutting themes and three strategic questions for our executive team to discuss.”
Rationale
This activity operationalises Lesson 1 by turning environmental scanning into a live, repeatable leadership artefact. Learners experience the shift from raw information to strategic insight, learning to frame scanning as an ongoing, dialogic process rather than a static report. By asking AI to separate strong from weak signals and then challenge its own synthesis, participants practise critical engagement with AI outputs, acknowledging both their value and their limitations. The exercise also surfaces governance and ethical questions, such as data sources, geographical bias, and transparency. Ultimately, the activity builds institutional intelligence by embedding scanning into leadership routines rather than treating it as a separate research function.
Transfer & Adaptation Tasks
Task 1 – Different HE Context
Adapt the briefing for a small, specialist higher education provider (for example, an arts college or a conservatoire). Redesign the scanning domains, sources, and implications to reflect their niche positioning, scale, and resource constraints, then compare the resulting briefings.
Task 2 – Cross-Domain Adaptation
Translate the same workflow for a research council or health-service education partner. Use AI to explore how environmental scanning might work in that context, and co-create a joint briefing that highlights interdependencies between the university and its wider ecosystem.
Faculty Trend Clinic: Reimagining a Programme Portfolio (Faculty Education Lead)
Objective
To use AI-assisted trend analysis to review and reshape a faculty-level programme portfolio in light of emerging sector trends.
Task
You are a Faculty Education Lead tasked with reviewing your faculty’s taught programme portfolio over the next five years. Begin by selecting one discipline area (for example, public health, computing, or business). Using a generative AI assistant, conduct a targeted trend analysis focused on three dimensions: learner expectations, labour market shifts, and pedagogical innovation.
Ask the AI to generate a concise “trend landscape” for your discipline, drawing on international sources and highlighting where UK developments align or diverge. Then request a second pass that focuses specifically on tensions and uncertainties—areas where the data is mixed, contested, or incomplete.
Next, create a “Faculty Trend Clinic” document. With AI support, map the identified trends against your current programme portfolio: where are you clearly aligned, where are you lagging, and where might you be over-invested in declining areas? Ask the AI to suggest three plausible programme-level responses, such as new microcredentials, redesigned core modules, or alternative delivery modes.
Finally, select one existing programme and use the AI to co-design a short internal briefing for its course team. The briefing should summarise relevant trends, suggest three concrete design questions for the next curriculum review, and outline options for low-risk experimentation.
Example Prompt
“You are supporting a Faculty Education Lead in a UK university. Using international data on higher education and labour markets, generate a 1,500-word ‘Trend Landscape’ for postgraduate public health programmes over the next five years. Focus on learner expectations, labour market needs, and pedagogical innovation. Identify at least five key trends, three tensions or uncertainties, and suggest how these might affect our existing portfolio of programmes in a research-intensive UK institution.”
Rationale
This activity operationalises Lesson 2 by connecting abstract trend analysis directly to programme-level decisions. Participants practise moving from broad, AI-generated landscapes to targeted implications for a specific faculty, reinforcing the distinction between description (“what’s happening”) and strategic response (“what might we do?”). By staging a second pass focused on tensions, they learn to resist overly neat narratives and treat AI outputs as prompts for debate rather than final conclusions. The Trend Clinic format also models a practical artefact that can be shared with programme teams, supporting distributed “trend literacy” across the institution.
Transfer & Adaptation Tasks
Task 1 – Different HE Context
Repeat the activity for a post-92 university with a strong widening participation mission. Ask the AI to emphasise local and regional trends, then compare how the resulting portfolio implications differ from those of a research-intensive institution.
Task 2 – Cross-Domain Adaptation
Adapt the Trend Clinic to an interdisciplinary area such as climate and health, data science and ethics, or creative computing. Use AI to explore how cross-sector trends intersect, and design a briefing that encourages collaboration across departments rather than isolated programme tweaks.
Branching Scenario Workshop: Co-Creating Futures with AI (Academic Board Member)
Objective
To facilitate a participatory scenario planning workshop using generative AI to co-create and explore branching futures for the institution.
Task
You are an Academic Board member leading a half-day workshop on “Our University in 2040.” Design a branching scenario exercise in which participants interact with a generative AI assistant to co-create and navigate multiple futures.
Begin by working with the AI to generate three high-level scenario seeds based on STEEPC/PESTLE drivers: for example, “Regulated AI Renaissance,” “Fragmented Microcampus World,” and “Community-Embedded University.” For each scenario seed, ask the AI to produce a short narrative vignette that includes voices from students, staff, and external partners.
In the workshop, divide participants into three mixed-role groups. Each group picks one scenario seed and, using a shared AI workspace, iteratively questions and refines it. Participants take turns entering prompts in a dialogue format (for example, “Dean of Education,” “Student Representative,” “Professional Services Manager”) and asking the AI to extend, challenge, or complicate the narrative. Every three or four turns, the group introduces a branching choice: a strategic decision the university could make (such as investing in AI-enabled student support or prioritising regional partnerships over global recruitment). The AI then generates divergent branches of the scenario based on these decisions.
After 45–60 minutes, each group extracts three key strategic tensions from their branching scenario. They then use the AI to help translate these tensions into strategic questions for Academic Board and senior leadership, ready for further debate.
Example Prompt
“You are facilitating a participatory scenario planning workshop for a UK university’s Academic Board. Generate three distinct 2040 scenario seeds, each 400–500 words, using STEEPC drivers. For each, include short fictional quotes from a student, an academic, and a professional services colleague. The scenarios should be divergent, plausible, and relevant to UK higher education. End each scenario with two branching decision points the institution could choose between.”
Rationale
This activity brings Lesson 3 to life by emphasising AI as a co-narrator and critical friend, not merely an analyst. The branching structure forces participants to see scenarios as dynamic and contingent on institutional choices, while the dialogue format foregrounds multiple stakeholder perspectives. By scripting prompts from different roles, participants experience how AI can support plural, sometimes conflicting viewpoints rather than collapsing them into a single narrative. The translation step—from branching futures to strategic questions—helps prevent scenario work from remaining abstract, anchoring it in governance and decision-making. The exercise also surfaces ethical considerations around whose futures are foregrounded, and how AI may amplify or mute particular voices.
Transfer & Adaptation Tasks Task 1 – Different HE Context
Repeat the branching scenario workshop for a small, specialist institution (such as an art school or teacher training college). Ask the AI to foreground professional identity, local community roles, and sector consolidation pressures, then compare the resulting tensions.
Task 2 – Cross-Domain Adaptation
Adapt the exercise for a joint workshop involving a university and a partner NHS trust, research institute, or FE college. Use AI to generate shared futures that highlight interdependencies, and co-create strategic questions that require joint action rather than isolated institutional responses.
Simulation Lab: Stress-Testing a Digital Transformation Plan (Institutional Research and Planning Analyst)
Objective:
To design and run an AI-supported stress test of a major institutional plan, identifying vulnerabilities and adaptive options under multiple simulated shocks.
Task:
You are an Institutional Research and Planning Analyst asked to stress-test your university’s five-year digital transformation strategy. Start by summarising, with AI support, the core assumptions of the strategy across four dimensions: technology, finance, staff capability, and student experience. Use the AI to help you express these assumptions in clear, testable statements (for example, “International enrolments will remain stable,” “Staff will adopt new AI tools within two years”).
Next, work with the AI to design three stress scenarios: a funding shock, a data ethics controversy, and an infrastructural disruption (such as a major system outage or cyber incident). For each scenario, ask the AI to outline a narrative describing how the shock emerges, how it interacts with your strategy’s assumptions, and how different stakeholder groups respond over a three-year period.
Using these narratives, construct a simple simulation lab. For each stress scenario, ask the AI to map the likely impacts on key institutional functions (such as teaching quality, student satisfaction, research performance, staff workload, and regulatory compliance). Then prompt it to identify three potential mitigation or adaptation strategies, and to estimate qualitatively which strategies are “robust across all scenarios,” “helpful but context-specific,” or “high risk/high reward.”
Finally, produce an internal briefing for the executive team that summarises the simulations, highlights the most exposed assumptions, and proposes a small number of priority resilience measures, such as alternative procurement models, staff development investments, or new governance mechanisms.
Example Prompt
“You are supporting an Institutional Research and Planning Analyst at a UK university to stress-test a five-year digital transformation plan. Based on common sector assumptions, generate three plausible stress scenarios: a funding shock, a data ethics controversy, and a major system disruption. For each, describe likely impacts on teaching, student experience, research, staff workload, and regulatory compliance, and suggest three adaptation strategies. Indicate which strategies appear robust across all three scenarios.”
Rationale
This activity translates Lesson 4 into a concrete governance tool, showing how generative AI can transform static plans into dynamic objects of inquiry. Participants practise making assumptions explicit, a critical step often skipped in strategic documents. By asking AI to generate stress scenarios and explore impacts, they learn to view plans as hypotheses rather than certainties. The focus on cross-scenario robustness supports more thoughtful risk management, while the requirement to produce an executive briefing reinforces communication and synthesis skills. The activity also raises ethical questions about how simulations portray staff and students, and how AI might inadvertently normalise certain responses while sidelining others.
Transfer & Adaptation Tasks Task 1 – Different HE Context
Apply the same method to stress-test a smaller-scale plan, such as a new online MSc or a collaborative doctoral training centre. Compare how stress scenarios play out when the stakes and time horizons are different, and adjust the resilience strategies accordingly.
Task 2 – Cross-Domain Adaptation
Adapt the Simulation Lab for a non-academic partner, such as a local authority or health provider working with the university. Use AI to co-create joint stress scenarios that affect both organisations, then identify where shared resilience measures could be more effective than isolated responses.
Foresight-to-Action Studio: Aligning Scenarios with Operational KPIs (Head of Department or Operations Manager)
Objective:
To link strategic foresight outputs to operational planning, creating AI-assisted pathways from long-range scenarios to departmental KPIs and improvement projects.
Task:
You are a Head of Department (or Operations Manager) in a UK university that has recently completed an institutional foresight exercise using AI-supported scenarios. Your brief is to translate these scenarios into concrete departmental actions and measures over the next three years.
Begin by selecting two contrasting institutional scenarios from the foresight work (for example, “Digitally Hybrid University” and “Regionally Embedded Civic Anchor”). Using a generative AI assistant, ask for a concise summary of each scenario, focusing on implications for your department’s teaching, research, student support, and partnerships.
Next, create a “Foresight-to-Action Canvas” for your department. With AI support, identify three strategic themes that appear across both scenarios (for example, AI literacy, flexible curriculum structures, or community engagement). For each theme, ask the AI to suggest possible departmental-level initiatives that would be valuable under both futures, and to propose candidate KPIs or indicators that might track progress.
Then, choose one theme and develop it into a mini action plan. Use the AI to help you specify objectives, potential activities, required resources, and risks. Ask it to draft a short narrative explaining how this plan responds to both scenarios, making it suitable for inclusion in faculty or institutional planning documents.
Finally, run a critical check by prompting the AI to identify possible blind spots, equity concerns, or unintended consequences of your chosen initiatives. Adjust your action plan and KPIs in light of these reflections, and document what trade-offs you are making.
Example Prompt
“You are advising a Head of Department at a UK university. The institution has two foresight scenarios: ‘Digitally Hybrid University’ and ‘Regionally Embedded Civic Anchor.’ Summarise each scenario in 300 words, focusing on implications for departmental teaching, research, student support, and partnerships. Then propose three strategic themes that are important in both futures, and suggest one departmental initiative and two potential KPIs for each theme.”
Rationale
This activity enacts Lesson 5 by treating AI as a translator between visionary foresight and everyday operations. Participants practise mapping high-level scenarios onto their own sphere of control, avoiding the common trap where foresight remains abstract and disconnected from planning cycles. The Foresight-to-Action Canvas encourages balanced attention to teaching, research, and civic roles, while the KPI design step forces operational specificity without collapsing uncertainty into rigid targets. The final critical check highlights the importance of ethical reflexivity, asking how AI-supported planning might reproduce or challenge existing inequities. Overall, the activity builds confidence in using AI to support institutional intelligence that is both future-facing and grounded.
Transfer & Adaptation Tasks
Task 1 – Different HE Context
Repeat the exercise from the perspective of a central professional services unit, such as student services, estates, or IT. Use AI to reinterpret the same institutional scenarios through their lens and design unit-specific initiatives and indicators.
Task 2 – Cross-Domain Adaptation
Adapt the Foresight-to-Action Studio for an interdisciplinary centre (for example, a sustainability institute or digital futures hub) that spans multiple faculties. Use AI to help align foresight themes with cross-faculty collaboration projects and shared KPIs that encourage collective, rather than siloed, action.
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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