2.2.1 Empathy-driven Research Techniques

This subtopic covers Empathy-driven Research Techniques such as interviews, surveys, empathy mapping, contextual inquiry, and observations provide valuable insights into learners’ backgrounds, goals, challenges, and needs. This research helps learning engineers develop a nuanced understanding of the learner population.

From the Learning Engineering Toolkit

Empathy-driven research takes place before design work starts, in the early phase the Toolkit devotes to investigative empathy, where the team gets to know learners' interests, abilities, stage of development, likely prior knowledge, daily experiences, beliefs, and support needs [LET-03]. This foundation borrows from design thinking, which cares deeply about why people act as they do and spends a large share of its inspiration phase building empathy with the population [LET-03]. Practitioners of design thinking have created empathy-building tools such as personas, customer journey maps, and service blueprints [LET-03].

The chapter outlines a number of ways design teams engage end users, both to understand them and to try out designs still in progress: interviews as one-on-one conversations with members of the population; surveys and questionnaires that reveal needs and desires; focus groups run around a prepared set of questions; card sorting, in which end users sort topics into groupings that make sense to them; and contextual inquiry, where a researcher observes end users in their own settings to learn their habits and interests [LET-03]. What these methods surface feeds straight into personas, which are first drafted during the early empathy work and reshaped over successive iterations as the team learns more [LET-03].

The companion tools chapter sorts these research approaches into qualitative and quantitative groupings for assessing stakeholder needs [LET-12]. The qualitative side centers on drawing out personal observations, thoughts, and experiences, and covers techniques like eliciting input through interviews or focus groups, open-ended or attitude surveys, behavioral or cognitive task analyses, and field observation, ethnography, and participatory study [LET-12]. The quantitative side leans on more objective tools, such as Likert-scale questionnaires with fixed response options and reviews of already-published research [LET-12]. The chapter urges teams to grasp the full spread of variation in a population instead of aiming at some notional typical learner, and supplies a set of guiding questions—who the learners are, what interests and background knowledge they bring, how widely their abilities range, and where and when learning will happen [LET-12]. It also cautions that any work with stakeholders, even a basic interview, has to take research ethics into account [LET-12].

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.