Idea Generation — Introduction

Student at a computer brainstorming with GenAI If you get stuck as you work through this in-class exercise, ask the instructor. Have fun—this one is hands-on.

Reminder on academic integrity

You must have permission from your instructor to use GenAI in any assessed work. Some courses forbid it; others allow limited or full use. Follow your course outline and citation rules. Using GenAI without permission is academic misconduct under UVic’s Academic Integrity Policy.

What you’ll learn

A simple, repeatable ideation pipeline: diverge → cluster → converge.
How to turn topics into measurable research questions.
How to summarize safely (tool limits, chunking, verification).

Before you start

Open one tool: Perplexity, Copilot, ChatGPT, Gemini, Claude, or Meta.ai (any is fine).

Safety: Don’t paste confidential/personal data. Redact names/emails/IDs (e.g., [Researcher_A], [Email_1]).

A. Diverge (5–7 min): create many options

Goal: generate a broad list of plausible directions without judging them yet.

Prompt (copy/paste, replace angle brackets): Role: Research mentor Action: Propose 20 distinct topic ideas about . Format: Table with columns = Idea, One-sentence rationale, Tags(3). Constraints: No duplicates; vary methods (survey, experiment, case study), contexts (K–12/higher ed/industry), and scales (classroom/institution/community).

Now try your own: If 20 is too many, ask for 10. If it’s too generic, add constraints (e.g., “Canada only,” “undergraduate makerspaces,” “low-cost data”).

B. Cluster (5–7 min): find patterns

Goal: reduce noise by grouping related ideas.

Prompt: Action: Cluster the 20 ideas into 5 labeled themes. Format: Table with columns = Theme, Ideas included, Why this theme matters (≤20 words). Constraints: No theme overlaps; each idea appears once.

Optional follow-up: Action: For each theme, generate 2 “stretch” variations that increase novelty without losing feasibility.

C. Converge (8–10 min): pick winners with criteria

Goal: select 2–3 ideas worth pursuing using measurable criteria.

Prompt: Action: Score each theme 0–5 on Novelty, Feasibility (skills/time), Data availability, Scope fit, Potential impact. Format: Table with columns = Theme, Novelty, Feasibility, Data, Scope, Impact, Notes. Constraints: Brief notes with concrete risks or assumptions.

Decide: Keep the top 2–3 themes. Write 1–2 risks you will check next (e.g., “access to participants,” “IRB/ethics needed,” “data exists?”).

D. Turn topics into measurable research questions (RQ drill)

Goal: convert a theme into sharp questions you can test/evaluate.

Prompt:

Role: Research methods tutor Action: Propose 5 research questions for the theme . Format: Table with columns = RQ, Variables/constructs, Method (1 line), Measurable outcome, Feasibility note. Constraints: Use clear, observable outcomes; avoid vague verbs; align with an undergraduate project scope.

Refine one RQ with acceptance criteria: Action: Rewrite RQ #2 so it is specific and measurable. Constraints: Time-bounded; identifies population and setting; feasible data source; ethical to implement.

E. Summarize a document safely (tool limits & chunking)

Free tiers and some enterprise tools limit input size. If a tool can’t “read” the whole article, it should say so. Ask it to be explicit—and chunk long text.

1) Pick an article (example news story):
LEGO helps Langford man recapture life after induced coma in 2018

2) Ask the tool to confirm capacity first:

Can you summarize the article at this URL? If not, say “TOO LONG” and ask me to paste text in chunks of your preferred size. URL: https://www.saanichnews.com/local-news/lego-helps-langford-man-recapture-life-after-induced-coma-in-2018-7333837

3) If too long, paste in chunks:
Here is CHUNK 1/3 (do not summarize yet). Acknowledge receipt only.

(Repeat for CHUNK 2/3 and 3/3.)

4) Then summarize across chunks: Action: Produce a 5-bullet summary using only the content from CHUNKS 1–3. Constraints: No new facts; quote 2 short phrases (≤10 words) with bullet numbers.

5) Compare summaries: Are the “quick” and “expanded” versions different in quality/coverage?

Guided examples you can try right now

1) Topic exploration (makerspaces)

I am an undergraduate student starting an honours project about university makerspaces. Generate 15 distinct topics. Vary method (survey/experiment/case), context (course/program/library), and outcome (learning/employability/access). Return a table with Idea, Rationale, Tags.

Now try a topic you care about.

2) Research questions from a bullet Suggest 5 research questions with measurable outcomes for: “Evaluate the effectiveness of makerspace programs.” Return a table with RQ, Variables, Method, Outcome, Feasibility note.

Then run the RQ drill above to tighten one question.

3) Article summary (with limits)

Ask for capacity, then either summarize the URL or paste chunks.
Follow with: Expand to ≥5 bullets; keep quotes short; no new facts.

Note: Limits change over time. If a tool doesn’t acknowledge limits, assume it may not have processed everything—ask it to show its work.

Reflection (2–3 min)

Which two criteria mattered most for your down-selection?
Which prompt change improved quality the most—tone, scope, or format?
What risk will you check next (data, ethics, access)?

Badge evidence: Save a screenshot of your “top 3” table (with scores) or your “5 research questions” table.

Self-check (2 min)

Did you diverge (≥10 ideas), cluster (clear themes), and converge (scored & selected)?
Are your RQs measurable and scoped to your time/resources?
Did you verify at least one claim or data assumption?

Go further

For more prompting techniques (tone, examples, format control), a short overview you can skim next:
Prompt engineering techniques

NEXT STEP: Critical Review