2.4.1 Types of Prototypes

This subtopic covers types of prototypes including:

• Low-Fidelity Prototypes: Simple representations, such as wireframes, storyboards, or paper prototypes, allow rapid testing of initial ideas. Low-fidelity prototypes are valuable in the early stages, where quick feedback and iteration are needed.

• High-Fidelity Prototypes: More detailed models, often interactive, that simulate the look and feel of the final product. High-fidelity prototypes are used later in the design process to test usability and functionality more accurately.

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From the Learning Engineering Toolkit

The Toolkit presents prototypes as spanning a range of fidelity. It treats a prototype as a preliminary version of a design that lets its concepts be shown and demonstrated, running from rough paper sketches at the low end to interactive, high-fidelity models that demonstrate features on a limited slice of content at the high end. [LET-12]

At the low-fidelity end are paper prototypes and wireframes; early on it helps to make fast, cheap versions like hand-drawn sketches. [LET-03], [LET-12] Mid-fidelity work might involve wireframes—interface sketches produced by hand or in a digital design tool—and storyboards that lay out how an experience unfolds over time. [LET-12] High-fidelity prototypes are usually working, though stripped down or partial; the human-centered chapter puts them on a scale from visual mock-ups that show what the system looks like to interactive versions that behave much like the finished system, built with only as much code or functionality as the current cycle's questions require. [LET-03] A more advanced software prototype, for instance, might be a fully working application covering a single learning module inside a curriculum. [LET-12]

The Toolkit also observes that a prototype can portray a design in a fixed, static form or can be made to mimic the actions and interactions of a proposed product. [LET-03] The Math Readiness example shows how fidelity gets chosen in practice: when the team did not have the resources to revise a digital build, it created and tried out a paper version of the Pattern Pathway game before going back to a digital one. [LET-03]

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.