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Authored from the Learning Engineering Toolkit — pending expert review

This page was authored on 2026-07-16 directly from the source text of the Learning Engineering Toolkit (Jim Goodell & Janet Kolodner (Eds.), 2023). Every factual claim carries an inline <cite> citation to the specific chapter it draws on; the full references are listed at the foot of the page. The prose is grounded in the primary source but has not yet been validated by a subject-matter expert. Use the Edit button to validate, correct, or expand.

Chapters: LET-03 (Chapter 3), LET-12 (Chapter 12)

2.4.3.1 Rapid Prototyping

Source:lebok.wiki/en/knowledge-area-2-human-centered-design-foundations/2-4-prototyping/2-4-3-rapid-prototyping-and-agile-methodologies/2-4-3-1-rapid-prototyping
Retrieved:2026-04-17
Confidence:HIGH

This approach emphasizes creating prototypes quickly, receiving immediate feedback, and making rapid adjustments. Rapid prototyping supports continuous iteration and refinement.

From the Learning Engineering Toolkit

Rapid prototyping is among the activities the Toolkit lists for human-centered design: its purpose is to give design ideas concrete form so they can be put in front of end users and other stakeholders for comment and testing. [LET-03] Ideas can be critiqued, tested, and improved only once they are solid enough for the whole team, stakeholders, clients, and end users to share one understanding of the design underway, and prototyping is what produces that shared understanding. [LET-03] A prototype gives ideas concrete form and provides the assurance needed to judge how fully the requirements have been met. [LET-03]

The Toolkit characterizes a prototype as a preliminary version of a design that makes it possible to demonstrate its concepts. [LET-12] Prototypes cover a spread of fidelity: from low-fidelity paper versions and wireframes, through visual mock-ups, to interactive high-fidelity versions that behave much like the real system. [LET-03] Early on it pays to make fast, cheap prototypes like hand-drawn sketches; as the work advances they grow steadily more realistic, passing through mid-fidelity wireframes and storyboards to working high-fidelity builds. [LET-12]

Speed is the whole idea. A design-thinking saying holds that teams should prototype early and repeatedly, since giving an idea concrete form lets them spot design problems soon and line up stakeholder support before much has been invested. [LET-03] The Toolkit passes along IDEO's suggestion to find quick, improvised ways to run your testing. [LET-12] Prototyping repeats on each cycle, with the relevant end users and stakeholders using every new prototype to critique or test the design. [LET-03] The Math Readiness case demonstrates this: when the team could not spare the resources to rework a digital build around a new playground theme, it just made a paper prototype so testing could continue. [LET-03]

Sources from the Learning Engineering Toolkit

  1. [LET-03]Khanh-Phuong Thai, Scotty D. Craig, Jim Goodell, Jodi Lis, Jordan Richard Schoenherr & Janet Kolodner (2023). Chapter 3: Learning Engineering is Human-Centered. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 83–124). Routledge / Taylor & Francis. doi:10.4324/9781003276579
  2. [LET-12]Sae Schatz, Khanh-Phuong Thai, Scotty D. Craig, Jordan Richard Schoenherr, Jodi Lis & Janet Kolodner (2023). Chapter 12: Human-Centered Design Tools. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 279–301). Routledge / Taylor & Francis. doi:10.4324/9781003276579

Further Reading

Source: wrgr/lecommons — curated by the learning engineering community. Confidence: medium — lecommons-curated; not yet independently expert-validated in this context. To validate or challenge any item: use the Edit button on this page. Upgrading confidence from medium → high requires expert sign-off.

Landmark Academic Papers

  • Design principles for virtual humans in educational technology environments — Scotty D. Craig, Nicolaus L. Schroeder (2018). International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0148-y · ~150 citations · tier: contemporary

    Synthesized human-factors engineering principles for designing pedagogical agents. Showed that voice, appearance, and persona of virtual instructors systematically affect learner cognitive load and perceived credibility — establishing that LE must integrate psychological design alongside algorithmic design. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-009 · confidence: medium · expert-validated: false

  • The voice quality of pedagogical agent impacts learning and agent perceptions — Ryan F. Siegle, Scotty D. Craig (2024). Journal of Computer Assisted Learning · doi:10.1111/jcal.12997 · ~20 citations · tier: contemporary

    Empirically demonstrated that voice quality (prosody, naturalness, warmth) of a pedagogical agent significantly affects both learning outcomes and learner perception. Illustrates the meticulous human factors engineering required in modern AI-driven learning systems. Source: lecommons/landscape/data/papers.json · ID: LE-LS-AP-010 · confidence: medium · expert-validated: false

Key People

  • Scotty D. Craig (profile), Arizona State University (active 2000s–present) — Human factors researcher; virtual humans and pedagogical agents specialist

    Systematically investigated design principles for virtual humans and pedagogical agents in learning environments Source: lecommons/landscape/data/people.json · ID: LE-LS-PP-014 · confidence: medium · expert-validated: false

Organizations, Conferences & Journals

  • International Journal of STEM Education (journal) · link
    Source: lecommons/landscape/data/organizations.json · ID: LE-LS-JO-007 · confidence: medium · expert-validated: false

Programs & Initiatives

  • LENS @ JHU — Learning Engineering for Next-Generation Systems (PC) · link

    Concentration within JHU MEd in Learning Design & Technology. Targets practitioners in complex organizations: defense, healthcare, large-scale education. Grounded in human systems integration and learning engineering expertise. Capstone produces evidence dashboard, reproducible report, and governance/ethics plan. Unique JHU ecosystem: APL + Medicine + IEEE/ICICLE partnership. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-001 · confidence: medium · expert-validated: false

  • Learning Engineering Fellowship (CMU OLI) (PC) · link

    Nine-week intensive for educators and designers to apply learning science and data-informed methods to real educational products and contexts; part of OLI professional learning. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-075 · confidence: medium · expert-validated: false

  • ASU Learning Engineering Institute & Graduate Certificate (PC) · link

    Graduate certificate and research network fusing human systems engineering, design, and evidence to improve educational systems; connects students with Learning Engineering Research Network partners. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-079 · confidence: medium · expert-validated: false

  • Purdue School of Engineering Education (ENE) (PC) · link

    First-in-the-nation school of engineering education; graduate offerings include the online M.S. in Engineering Education, Ph.D. in engineering education research, and the stackable Teaching and Learning in Engineering graduate certificate—explicit “learning engineering” language appears in certificate and course titles. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-089 · confidence: medium · expert-validated: false

  • International Journal of STEM Education (CO) · link

    Discipline-based education research (DBER): problem-based learning, flipped classrooms, educational robotics, STEM learning outcomes at scale. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-130 · confidence: medium · expert-validated: false

  • Human Factors and Ergonomics Society (CO) · link

    Professional society for human factors and ergonomics. Publishes Human Factors journal and organizes the annual HFES conference. The Training Systems technical group directly addresses LE-relevant human factors in instructional systems. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-133 · confidence: medium · expert-validated: false

Lecommons enrichment applied 2026-04-17. All items pending expert validation. See wrgr/lecommons for source data and curation methodology.

←2.4.3 Rapid Prototyping and Agile Methodologies
2.4.3.2 Agile Design in Learning Engineering→