← Motivation InterventionsJiesi Guo
Design & procedure

How the trial worked

A 2 × 2 factorial randomized controlled trial crossing role modeling (present / absent) with goal setting (present / absent), delivered online in a single scheduled class session.

Figure 1. STEM intervention design and condition content

Panel A. Trial design and process


Panel B. Condition content and dose

Active control

  • Four TED-Ed videos on general-interest, non-STEM topics
  • Three engagement items after each video (12 in total), answered Yes/No or on a No–Yes slider:
    • attention, focus and concentration
    • engagement, mood and strength of feeling
    • ease of viewing and confidence
    • mental effort and thinking required
    • whether the video was a good use of time
  • General-content chatbot

Goal setting (GS) only

  • Three introductory videos
  • Task-value prompt
  • Effort, help-seeking and prior-goals questions
  • Worked SMART example
  • SMART goal construction
  • Goal review
  • Strategy choices and if–then plan
  • Goal-setting chatbot

Role modeling (RM) only

  • Five women-in-STEM role-model videos (ATSE)
  • Four career-rating items after each video:
    • confidence of succeeding in that career
    • interest in it
    • usefulness for future goals
    • manageability of its costs
  • Choice of the most inspiring video
  • Role-model chatbot

Combined

  • Abbreviated goal-setting sequence
  • One of three introductory videos (SMART goals)
  • Task-value prompt
  • Effort, help-seeking and prior-goals questions
  • Worked SMART example and SMART goal construction
  • Goal review, strategy choices and if–then plan omitted
  • Three of five role-model videos
  • Four career-rating items after each video, as in RM only
  • Combined chatbot
SEVT = situated expectancy-value theory; ATSE = Australian Academy of Technological Sciences and Engineering. All four conditions were delivered online within a single scheduled class session, with 35 minutes of intervention content per condition and a condition-specific chatbot available in every condition. Holding session time constant meant the combined condition received a reduced dose of both components. n = students analyzed per condition.
Content and dose

Exactly what each condition contained

Every condition had the same 35 minutes of content. To fit both components into that time, the combined condition used a shorter version of each: three of the five role-model videos, and the goal-setting sequence up to the SMART goal, without the goal review, strategy choices or if–then plan.

Table 1. Condition content and planned dose

Component elementActive controlGS onlyRM onlyCombined
Introductory videos (STEM importance, SMART goals, setting goals)—3 videos—1 of 3 (SMART goals)
Task-value prompt (open written response on STEM relevance)—1 prompt—1 prompt
Effort, help-seeking and prior-goals questions—3 items—3 items
SMART goal (5 elements, menu or own wording)—5 elements—5 elements (different menu options)
Worked SMART example (shown on the page)—1—1
Goal review (specific, timeframes, measurable, achievable, effort, realistic)—6 ratings—Not delivered
Strategy choices (distraction, difficulty)—2 items—Not delivered
Free-text rewrite of goal with if–then plan—6 fields—Not delivered
Closing items (obstacles, open to adjusting, confidence)—3 items—Not delivered
Role-model videos (ATSE, women in STEM, ≈ 3 min each)——5 videos3 videos
Post-video career ratings (4 items per video)——20 responses12 responses
Most inspiring video (single choice)——1 item1 item
Active-control videos (TED-Ed)4 videos———
Post-video response items (engagement, attention, ease, effort, use of time)12 items (3 per video)———
Condition-specific chatbot (GPT-4o)AvailableAvailableAvailableAvailable
SEVT survey (12 items × 4 domains, T1 and T2)48 × 248 × 248 × 248 × 2
Session length35 min35 min35 min35 min
Dash = not part of that condition; not delivered = an element of the single-component condition omitted from the combined condition. The combined condition’s goal menus offered a different and longer set of options (67 against 41 across the five SMART elements, each including “Other”). GS = goal setting; RM = role modeling; SEVT = situated expectancy-value theory.
Procedure

One class session, July–August 2025

Data were collected in six Victorian secondary schools during a single scheduled class session, using REDCap.

  1. Consent and assentParents consented through each school’s online consent platform; students gave assent before taking part.
  2. Baseline (T1)At the start of the session, students answered demographic questions and the SEVT motivation survey.
  3. RandomizationEach student was individually randomized within their class with REDCap’s randomization module, using an unblocked allocation table generated by computer script before data collection. Research staff could not view the table or anticipate the next allocation, and students and teachers were not told which condition a student received.
  4. InterventionStudents worked through 35 minutes of online content for their condition, with a condition-specific chatbot available. A member of the research team was present to help with technical issues, and students stayed on each role-model video page for at least one minute before moving on.
  5. Posttest (T2)Immediately afterwards, in the same session, students answered two manipulation-check items and completed the SEVT survey again.

Newcomers. Students who missed the scheduled session (e.g., excursions, illness or technical problems) completed the same sequence when they next attended and were randomized the same way. These 192 students (22.5%) were evenly spread across conditions (χ²(3) = 0.57, p = .904) and school locations (χ²(1) = 2.11, p = .147), and newcomer status was included as a covariate.

Manipulation checks. Students who received goal-setting content were more likely to report having made a SMART goal than those who did not (90.0% vs. 36.7%), and students who received role-modeling content were more likely to report seeing a STEM role model (95.3% vs. 49.8%; both p < .001).

The chatbot

STEMGuide: a tightly scoped helper in every condition

How it was set up

  • A friendly assistant named “STEMGuide”, running on GPT-4o (OpenAI) and written for students under 18.
  • Answers shorter than 40 words, in simple, inclusive language.
  • Drew only on the videos the student had just watched; off-topic questions weren’t answered.
  • Scripted refusals for inappropriate content, in line with each school’s ICT guidelines; it never made up answers.

Focus by condition

  • Control: general questions about the video content.
  • Goal setting: connecting STEM to everyday life and future goals.
  • Role modeling: STEM careers, education requirements and skills.
  • Combined: both, for broader STEM exploration and career planning.
Measures

The SEVT motivation survey

Twelve items drawn from established scales (Kosovich et al., 2015; Gaspard et al., 2015; Flake et al., 2015) measured four beliefs from situated expectancy-value theory. Each item was asked separately for biology, chemistry, physics and STEM overall (48 responses per wave), on a 7-point scale from Strongly disagree to Strongly agree. The order of subject blocks and items was randomized. Reliability was good to excellent (α and ω = .83–.97).

Table 2. Survey items · [subject] replaced by each domain

ConstructItem
ExpectancyI know I can learn the material in [subject].
ExpectancyI believe that I can be successful in [subject].
ExpectancyI am confident that I can understand the material in [subject].
Intrinsic value[Subject] is fun for me.
Intrinsic valueI enjoy doing [subject].
Intrinsic value[Subject] is interesting.
Utility valueI think [subject] is important.
Utility valueI value [subject].
Utility valueI think [subject] is useful.
Cost[Subject] requires too much time.
CostI have to give up too much to do well in [subject].
Emotional cost[Subject] is too stressful.
Cost items were reverse-scored for analysis, so higher scores mean lower perceived cost.
Design notes

Registration and analysis

Registration and sample

  • Hypotheses and analysis plan preregistered on OSF on 5 June 2026, after data collection but before the research team accessed the data.
  • 865 students randomized; 855 analyzed (modified intention-to-treat; 10 students reporting a nonbinary gender identity could not be included in models with gender as a binary covariate).
  • Four departures from the registration are reported in the preprint, including the added manipulation checks and the location analyses added after registration.
  • Approved by the Australian Catholic University Human Research Ethics Committee (2024-3913H).

Analysis

  • Longitudinal latent SEM (lavaan 0.6-20, R 4.4.1) with scalar invariance across time and gender.
  • Each T2 outcome adjusted for its T1 level, gender, newcomer status and school location (rural vs. urban).
  • Robust ML with FIML for missing data; school-clustered standard errors; Benjamini–Hochberg correction within outcome families.
  • A complete-case sensitivity analysis (N = 570) reproduced the main pattern.