The Creative Team Member: A Study of the Influence of AI Integration Timing on Brainstorming and Ideation
2026년 7월 11일 업데이트: Mohit Bhandari, McMaster University
The Effect of Artificial Intelligence Integration Timing on Group Brainstorming and Ideation in Undergraduate Students: A Randomized Controlled Trial
This study aims to examine how the timing of artificial intelligence (AI) integration during group brainstorming activities affects the creativity and ideation of participants.
Undergraduate student participants will be randomly allocated into teams of four to complete a structured 12-minute brainstorming task.
The study utilizes a three-group experimental design to compare three conditions: no AI access, AI access from the outset of the session, and AI access introduced halfway through the session.
The standardized generative AI tool used is ChatGPT Pro.
The creative output produced by each group will be evaluated by a panel of independent expert judges using the Consensual Assessment Technique (CAT).
연구 개요
상태
아직 모집하지 않음
정황
상세 설명
Artificial intelligence (AI) tools are increasingly used in academic and professional environments, yet little is known about how the timing of AI's introduction during a collaborative brainstorming process shapes the creativity of the ideas produced. While recent literature suggests that human-AI collaboration can increase the likelihood of successful idea generation when AI is present throughout an entire session, the specific effect of integration timing remains a gap in the literature. This study aims to provide early data on whether AI functions more effectively as an early collaborator, a later resource, or whether its absence supports more creative thinking.
The study recruits undergraduate students enrolled in a summer research program, who are then randomly assigned to teams of four using a digital randomizer. Over the course of nine sessions, these teams participate in structured, 12-minute collaborative brainstorming tasks focused on designing strategies to address common university student challenges. To mitigate the influence of individual baseline characteristics on the group output, participants are re-randomized into new teams for each separate session.
To standardize the technological intervention and eliminate technical confounders, the study utilizes ChatGPT Pro (GPT-4o architecture) on pre-set devices. At the conclusion of the timed task, a designated team scribe submits the group's collaboratively generated idea list and their selected best idea via a secure, digital case report form.
Following data collection, all session outputs are immediately de-identified and assigned neutral identifiers. A panel of independent expert judges, blinded to the condition assignments, evaluate the output of each group.
연구 유형
중재적
등록 (추정된)
16
단계
- 해당 없음
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 연락처
- 이름: Alexandra Leone, BSc
- 전화번호: 44490 905-527-4322
- 이메일: leonea5@mcmaster.ca
연구 연락처 백업
- 이름: Kaya Bhandari
- 이메일: bhandk5@mcmaster.ca
연구 장소
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Ontario
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Hamilton, Ontario, 캐나다, L8S 4L8
- McMaster University
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연락하다:
- Kaya Bhandari
- 이메일: bhandk5@mcmaster.ca
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연락하다:
- Alexandra Leone, BSc.
- 전화번호: 44490 905-527-4322
- 이메일: leonea5@mcmaster.ca
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부수사관:
- Kaya Bhandari
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부수사관:
- Alexandra Leone, BSc
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수석 연구원:
- Mohit Bhandari, MD, PhD, FRCSC
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
공부할 수 있는 나이
- 어린이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
예
설명
Inclusion Criteria:
- Enrolled as a student in the designated summer research program
- Able to provide voluntary informed consent
- Proficient in reading and communicating in English (required to engage with the scenarios and the artificial intelligence tool)
Exclusion Criteria:
-Not enrolled as an undergraduate student
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 다른
- 할당: 무작위
- 중재 모델: 병렬 할당
- 마스킹: 하나의
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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간섭 없음: No Artificial Intelligence Tools (Control)
Across nine separate sessions, teams of four participants will complete all nine distinct student-struggle scenarios (one scenario per 12-minute session).
In each session, participants engage in a structured collaborative brainstorming task entirely without access to artificial intelligence tools for the full 12 minutes.
At the conclusion of each session, the team must evaluate their generated list and select their single best idea to solve that specific scenario.
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실험적: Artificial Intelligence First (Early Integration)
Across nine separate sessions, teams of four participants will complete all nine distinct student-struggle scenarios (one scenario per 12-minute session).
In each session, at minute 0, participants input the scenario prompt into ChatGPT Pro (GPT-4o architecture).
The team receives a single initial output of ideas and does not engage in further back-and-forth dialogue with the tool.
Participants use this initial artificial intelligence response as a baseline to continue human brainstorming and ideation for the remainder of the 12 minutes, ultimately selecting and submitting their single best idea for that specific scenario.
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The introduction of ChatGPT Pro (GPT-4o architecture) at the beginning (minute 0) of a 12-minute group brainstorming session.
A single prompt containing the scenario is submitted to generate an initial set of ideas, with no further back-and-forth dialogue permitted.
This single artificial intelligence output serves as the baseline for the team's subsequent human ideation.
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실험적: Artificial Intelligence Last (Late Integration)
Across nine separate sessions, teams of four participants will complete all nine distinct student-struggle scenarios (one scenario per 12-minute session).
In each session, participants brainstorm independently without technology for the first 6 minutes.
At minute 6, the team inputs the scenario prompt along with their already-generated human ideas into ChatGPT Pro (GPT-4o architecture).
Receiving a single output with no further back-and-forth dialogue, the team uses the artificial intelligence response to continue ideating for the final 6 minutes, ultimately selecting and submitting their single best idea for that specific scenario.
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The introduction of ChatGPT Pro (GPT-4o architecture) at the midpoint (minute 6) of a 12-minute group brainstorming session.
A single prompt containing both the scenario and the team's already-generated human ideas is submitted, with no further back-and-forth dialogue permitted.
This single artificial intelligence output is used to guide the team's human ideation for the remaining 6 minutes.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Creativity Score of the Selected Best Idea
기간: Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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The primary outcome is the mean expert creativity rating assigned to each team's top idea, across each of the 9 sessions, as assessed by independent judges using the Consensual Assessment Technique (CAT).
Higher average scores denote a more creative output, while lower scores denote a less creative output.
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Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Overall Creativity Score of Full Idea List
기간: Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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The collective creative quality of the entire list of ideas generated by the team during the session, evaluated by independent expert judges using the Consensual Assessment Technique (CAT).
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Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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Number of Ideas Generated During the 12-Minute Session
기간: Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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The total number of ideas generated (fluency) will be reported with the explicit methodological limitation that AI generation speed inherently outpaces human typing speed.
Fluency will be analysed purely observationally, as the 12-minute constraint does not control for the mechanical advantage of the AI tool.
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Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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Qualitative Resemblance of the Team's Output to Typical AI-Generated Responses
기간: Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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This will be subjectively assessed by the expert judges on a 1-5 scale (anchored from 'strongly AI-typical' to 'strongly human'), independent of the CAT creativity evaluation.
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Evaluated cross-sectionally upon the conclusion of each 12-minute session, across the 9 study sessions.
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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
수사관
- 수석 연구원: Mohit Bhandari, MD, PhD, FRCSC, McMaster University
간행물 및 유용한 링크
연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.
일반 간행물
- Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016 Jun;15(2):155-63. doi: 10.1016/j.jcm.2016.02.012. Epub 2016 Mar 31.
- Çelik, T. İ., & Akay, C. (2025). The Impact of Brainstorming Technique on Academic Achievement and Creative Thinking: A Meta-Analysis Study. Sage Open, 15(3). 62
- Muller, M., Houde, S., Gonzalez, G., Brimijoin, K., Ross, S. I., Silva Moran, D. A., & Weisz, J. D. (2024). Group brainstorming with an AI agent: Creating and selecting ideas. Proceedings of the 15th International Conference on Computational Creativity (ICCC 2024). https://computationalcreativity.net/iccc24/papers/ICCC24_paper_18.pdf
- Amabile, Teresa. (1982). Social psychology of creativity: A consensual assessment technique. Journal of Personality and Social Psychology. 43. 997-1013. 10.1037/0022-3514.43.5.997
- Zhang C, Shao Y, Yuan Y, Shen W. Artificial Intelligence Reshapes Creativity: A Multidimensional Evaluation. Psych J. 2025 Dec;14(6):831-840. doi: 10.1002/pchj.70042. Epub 2025 Aug 5.
- Lee BC, Chung JJ. An empirical investigation of the impact of ChatGPT on creativity. Nat Hum Behav. 2024 Oct;8(10):1906-1914. doi: 10.1038/s41562-024-01953-1. Epub 2024 Aug 12.
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (추정된)
2026년 7월 20일
기본 완료 (추정된)
2026년 8월 15일
연구 완료 (추정된)
2026년 9월 15일
연구 등록 날짜
최초 제출
2026년 7월 5일
QC 기준을 충족하는 최초 제출
2026년 7월 5일
처음 게시됨 (실제)
2026년 7월 9일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2026년 7월 14일
QC 기준을 충족하는 마지막 업데이트 제출
2026년 7월 11일
마지막으로 확인됨
2026년 7월 1일
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
- 20358
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
아니요
IPD 계획 설명
Individual participant data (IPD) will not be shared publicly due to an elevated risk of deductive re-identification.
While numeric outcomes could be anonymized, the primary data consists of raw qualitative text (transcribed group brainstorming ideas and solutions) generated by a highly specific, geographically localized cohort of undergraduate students enrolled in the 2026 summer research program.
Because the 2026 cohort size is small and the context is unique, sharing raw textual transcripts poses a risk of accidentally exposing the identities of the participants through specific phrasing, examples, or contextual clues, despite de-identification efforts.
Therefore, to ensure participant confidentiality in accordance with institutional ethics guidelines, the raw data will remain confidential within the primary research team.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
아니
미국 FDA 규제 기기 제품 연구
아니
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .