This subtopic covers methods to ensure that learning solutions can scale effectively across different contexts without disrupting the system’s balance, focusing on sustainable and adaptable designs.
From the Learning Engineering Toolkit
The Learning Engineering Toolkit casts scale as a defining engineering concern: it sets science, whose goal is to uncover truths, against engineering, whose goal is to build scalable solutions that hold up across a range of conditions [LET-04]. The chapter counts the capacity to carry out whole-system design aimed at sustainability among the profession's core competencies [LET-04]. Complex systems become scalable partly by being divided into modules joined by interfaces, and their interoperability improves when those interfaces follow shared standards [LET-04].
Cost sits at the heart of this scaling. Drawing on the Open edX example, the book shows how per-student, per-course, and per-platform costs compound at different rates: one dollar of per-student cost across a thousand students turns into a thousand dollars per course, and a thousand dollars of per-course delivery across a thousand courses swells to a million, whereas a thousand dollars spent once on platform development stays a thousand dollars [LET-04]. Because delivery costs accumulate steeply at scale, most of the engineering work went toward making course creation both cheap and effective, while the cost of building the platform drew comparatively little attention [LET-04]. Piotr Mitros borrowed methods from intelligent tutoring systems to hold down authoring costs without losing the cognitive payoff, and even had learners tag content rather than paying for more expensive machine intelligence [LET-04].
In the book's telling, sustainability also means designing for change and longevity. Engineers lean on models, like the civil engineer's models of population growth, material strength, and weather, to make sure a solution is appropriately scaled and endures across its working life, and the chapter argues that learning solutions must likewise hold up at every scale of use [LET-04]. The platform was built to improve iteratively from data, capturing every action and using a pluggable XBlocks architecture that allows continual extension [LET-04]. The chapter ends by contending that, just as engineering was required to mass-produce penicillin during World War II and to distribute COVID-19 vaccines, it will be required to build and scale the learning systems that future populations will need [LET-04].
Sources from the Learning Engineering Toolkit
- [LET-04]Avron Barr, Brandt Dargue, Jim Goodell & Brandt Redd (2023). Chapter 4: Learning Engineering is Engineering. In Jim Goodell & Janet Kolodner, Learning Engineering Toolkit (pp. 125–151). 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 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
Programs & Initiatives
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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
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INCOSE Systems Engineering Body of Knowledge (SEBoK) (CO) · link
Open wiki-based body of knowledge for systems engineering. Includes the Human Systems Integration knowledge area. The SE process framework (requirements, architecture, integration, verification) is the engineering process backbone that LE adapts for learning system development. Source: lecommons/site/src/data/programs_people_registry.json · ID: LE-PP-134 · 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.