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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)

2.2.3 Empathy Mapping

Source:lebok.wiki/en/knowledge-area-2-human-centered-design-foundations/2-2-empathy-driven-research-in-human-centered-design/2-2-3-empathy-mapping
Retrieved:2026-04-17
Confidence:HIGH

Definition: Empathy maps are visual tools used to organize insights about what learners say, think, feel, and do. They help synthesize qualitative research findings and deepen empathy for the learner’s perspective. Empathy mapping helps learning engineering teams identify specific pain points, motivations, and preferences, guiding the design of solutions that directly address learners’ experiences.

From the Learning Engineering Toolkit

The Toolkit does not itself employ the label empathy mapping; the nearest grounded material is its discussion of empathy in design thinking and of personas. As the Toolkit describes it, design thinking calls for empathizing with the population—coming to appreciate what they know, what they can do, and what they need and want. [LET-03] A large part of design thinking's inspiration phase is spent getting to know and empathizing with the population, and its practitioners have built methods like personas, customer journey maps, and service blueprints to support that empathy. [LET-03]

Personas are the Toolkit's main device for capturing what a team has learned about its learners. They are built from data gathered before design starts, during the early empathy work, and they distill recurring patterns in users' behavior, goals, and motivations into an invented profile of one representative person. [LET-03], [LET-12] A persona usually carries a name, a personality and set of traits, a cultural background, prior knowledge and experience, goals, things that motivate the person, and sources of frustration. [LET-12]

The Toolkit illustrates empathy work in action: the exploration phase of the Math Readiness project rested on design thinking's notion of empathy, seeking to understand the needs, interests, and abilities of the two- and three-year-olds it served by drawing on the research literature and play-test observation before assembling its Lily persona. [LET-03] Design justice adds a caution that personas ought to be grounded in the actual lived experiences of end users rather than in guesses about their abilities, needs, and frustrations. [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.

←2.2.2 Contextual Inquiry
2.3 Personas→