This topic addresses the use of personas in human-centered design. Personas represent archetypal users of a solution (e.g. learners) based on research insights, capturing key characteristics, needs, and motivations.
Personas guide the design process, ensuring that learning solutions address specific learner profiles. Key findings from empathy-driven research can translate into a manageable set of personas reflecting the intended learner populations and variety of contextual factors.
Personas can become powerful tools to inform collaborative design decisions and that learning engineering team members maintain shared understanding of learner needs. Personas ensure that solutions are tailored to real user needs when employed using generally accepted best practices, such as:
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Ground personas in research: Base personas on actual data from user research (interviews, surveys, observations) to ensure they reflect real user needs, goals, and pain points.
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Focus on behaviors, not demographics alone: Include users’ motivations, behaviors, and challenges over just age, gender, or other demographic details to make personas more actionable.
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Keep them specific and relevant: Make personas specific to your project, and avoid trying to represent all possible users. Choose characteristics that highlight key differences in needs and interactions.
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Use personas throughout the design process: Incorporate personas in brainstorming, ideation, prototyping, and testing to maintain focus on user needs and guide decision-making.
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Update personas as needed: Regularly refine personas based on new insights or changes in user behavior to keep them relevant over time.
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Encourage team-wide adoption: Share personas with everyone involved, from designers to stakeholders, to create a shared understanding of the users and ensure alignment on user goals.
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Bring personas to life: Use stories, visuals, and quotes to make personas feel real and relatable, helping team members empathize with the user experience.
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 frommedium→highrequires expert sign-off.
Landmark Academic Papers
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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
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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
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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
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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
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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
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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
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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
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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.