5.3.G Prompt Library
Estimated time: 30 minutes
Label: 5.3.G
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Learning objectives
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Prompt Library
Theme 1: AI-Augmented Environmental Scanning and Institutional Intelligence
Designing an AI-Assisted Scanning Workflow
You are the head of planning at a university. Ask an AI assistant to design a real-time environmental scanning workflow for your institution, covering policy, pedagogy, technology, demographics, funding, and sustainability. Instruct it to specify: priority data sources, automation options (e.g. APIs, dashboards, scheduled briefs), a weekly briefing format for senior leaders, and mechanisms for human verification and ethical oversight. Request the output as a clear step-by-step process plus a suggested template for the briefing.
Distinguishing Strong and Weak Signals
Prompt an AI assistant to analyse a recent month of higher education news, policy announcements, and sector commentary (you may paste or link summaries) and classify items into “strong signals” (well-evidenced trends) and “weak signals” (emerging uncertainties). Ask it to justify each classification, explain possible implications for your institution’s teaching, research, and student experience, and propose three questions your leadership team should discuss in response.
Building an Institutional Intelligence Dashboard
Ask an AI assistant to propose a design for an “institutional intelligence” dashboard that continuously updates decision-makers on relevant external developments. Instruct it to specify: key indicators to track, recommended visualisations, update frequency, roles responsible for interpretation, and how to integrate qualitative AI-generated summaries with quantitative metrics. Request practical examples tailored to a UK higher education setting.
Inclusive and Ethical Environmental Scanning
Invite an AI assistant to critically review your institution’s current environmental scanning practices (you may briefly describe them) for inclusivity, bias, and blind spots. Ask it to identify whose perspectives (regions, disciplines, communities) are likely under-represented in the data you monitor, and to recommend specific steps to diversify sources, increase transparency about models and methods, and engage staff and students in challenging AI-generated interpretations.
From Scanning to Strategic Conversation
Ask an AI assistant to turn a dense set of environmental scanning outputs (you may paste bullet points or a short briefing) into a facilitation guide for a 60-minute strategic discussion with senior leaders. Instruct it to generate: three framing narratives about “what seems to be changing,” four discussion questions that link signals to institutional priorities, and a simple sense-making activity that helps participants distinguish noise from genuinely strategic signals.
Theme 2: Generative Trend Analysis and Foresight Modelling
Mapping Global and Local Trends
Ask an AI assistant to identify and compare five global higher education trends and five local/national trends that could shape your institution over the next five years. Instruct it to: briefly define each trend, explain why it matters, and suggest one early indicator you could monitor. Request a final section that highlights where global and local patterns reinforce or contradict each other.
Turning Data Overload into Strategic Themes
Provide an AI assistant with a list of reports, datasets, or newsletters your institution regularly receives (by title or brief description). Ask it to infer the main trend categories these sources collectively point to (e.g. assessment, funding, student demand) and generate a concise thematic map. Instruct it to propose how each theme could be summarised in a one-page trend briefing for your academic board.
Generative Foresight Scenarios from Trend Data
Prompt an AI assistant as follows: “Using current trends in AI, internationalisation, funding, and student expectations, generate three distinct five-year futures for our university. For each future, describe the external environment, likely student behaviours, and dominant teaching and research models. Then identify two strategic opportunities and two risks for our institution in each scenario.”
Auditing Bias and Blind Spots in Trend Analysis
Ask an AI assistant to critically examine how a typical AI model might misrepresent trends in global higher education (e.g. over-emphasis on English-language sources, elite institutions, or Global North policy debates). Instruct it to list likely distortions, explain how these could skew institutional planning, and recommend practical prompt strategies and data-triangulation steps to reduce these risks in your own AI-supported trend work.
Designing an AI-Supported Trend Literacy Workshop
Ask an AI assistant to design a 90-minute “trend literacy” workshop for academic and professional services staff. Instruct it to include: learning objectives, a short activity where participants use an AI tool to generate and critique a trend map, a debrief on the difference between description and foresight, and a closing exercise where participants identify one concrete action they will take in their role based on trend insights.
Theme 3: AI-Supported Scenario Building and Participatory Foresight
Framing Questions for Scenario Planning
Ask an AI assistant to generate ten alternative framing questions for a scenario-planning exercise focused on the future of your institution in 2035. Instruct it to vary focus across international partnerships, digital learning ecosystems, funding models, academic labour, and student expectations, and to phrase questions in ways that invite multiple plausible answers rather than a single prediction.
- Constructing a 2x2 Scenario Matrix
Prompt an AI assistant to help you build a 2x2 scenario matrix for your university, using two high-impact, high-uncertainty drivers relevant to your context (for example, stringency of AI regulation and global student mobility). Ask it to: justify the choice of drivers, name and describe each of the four resulting futures, and suggest one narrative vignette (e.g. a short fictionalised student or staff perspective) that makes each future feel tangible.
Co-Creating Scenarios with Diverse Stakeholders
Ask an AI assistant to propose a participatory foresight workshop format in which students, academics, and professional staff co-create AI-supported scenarios for the future of learning and teaching. Instruct it to describe: pre-work (including AI prompts participants might use), small-group activities where AI generates scenario fragments, methods for combining outputs into coherent narratives, and reflection questions that foreground ethics, inclusion, and power.
Testing Scenario Coherence and Plausibility
Provide an AI assistant with a brief outline of one or more draft strategic scenarios (or describe them in prose). Ask it to identify internal inconsistencies, implausible assumptions, and areas where causal links are weak or unexplained. Instruct it to suggest revisions that improve coherence, highlight alternative interpretations, and ensure that each scenario remains challenging but plausible for a higher education setting.
Scenario Libraries as Living Institutional Assets
Ask an AI assistant to design a structure for a digital “scenario library” that your institution could maintain over time. Instruct it to describe: metadata to store with each scenario (e.g. drivers, time horizon, authors, equity considerations), how AI tools could periodically scan new developments and flag which scenarios they resemble, and governance arrangements to keep the library inclusive, updated, and used in decision-making.
Theme 4: Stress-Testing Plans and Linking Foresight to Operational Strategy
Stress-Testing a Strategic Plan with AI
Provide a brief summary of your institution’s current strategic plan (or one priority area). Ask an AI assistant to design a stress-test using three contrasting futures (e.g. economic downturn, rapid digital disruption, major policy reform). Instruct it to analyse how your plan performs under each future, identify vulnerabilities and hidden dependencies, and recommend specific adjustments to increase resilience.
Simulating Stakeholder Responses to Change
Ask an AI assistant to simulate how different stakeholders (students, academic staff, professional services, external partners) might respond to a major AI-enabled change initiative at your institution, such as adopting AI-supported assessment or student advising. Instruct it to generate realistic concerns, motivations, and unintended consequences, and to propose targeted engagement and communication strategies for each group.
Translating Foresight into KPIs and Projects
Prompt an AI assistant to take three existing foresight scenarios for your university (which you may summarise briefly) and translate them into operational terms. Ask it to propose: potential KPIs aligned with each scenario, concrete projects or pilots for the next 12–24 months, and which units (faculties, central services, committees) should own each action. Request a short commentary on which actions are “robust” across all scenarios.
Designing a Foresight–Operations Alignment Loop
Ask an AI assistant to design a recurring annual cycle that links environmental scanning, trend analysis, scenario building, stress-testing, and operational planning for your institution. Instruct it to specify: activities and outputs at each stage, how AI tools support them, decision points for senior governance, and feedback mechanisms that ensure learning from implementation reshapes future foresight.
Evaluating the Quality of AI-Supported Simulations
Ask an AI assistant to develop an evaluation rubric for AI-enabled simulations and stress-tests used in your institution’s strategic planning. Instruct it to include criteria for relevance, diversity of futures, transparency of assumptions, ethical integrity, and actionability of insights. Request guidance on how different committees (e.g. finance, education, research) could use this rubric to judge whether simulations meaningfully inform their decisions.
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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