About

H. Craig Heller is a professor in the Department of Biology at Stanford University and is part of the faculty in the Human Biology program. His research focuses on biological sciences, contributing to the academic community through teaching and scholarly activities. As a faculty member, he is involved in the development and delivery of undergraduate education in human biology, supporting the interdepartmental undergraduate program. His work is integral to advancing understanding in biological sciences within the context of human biology at Stanford.

Research topics

• Computer Science
• Cognitive science
• Human–computer interaction
• Psychology
• Mathematics education
• Cell biology
• Biology
• Genetics
• Immunology
• Endocrinology

Selected publications

• “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook

  International Journal of Educational Technology in Higher Education · 2022 · 62 citations

  Senior authorCorresponding
  • Computer Science
  • Mathematics education
  • Computer Science

  Abstract Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise learners’ mental capacities. In a 2-month study we investigated university students’ learning with an AI-enriched digital biology textbook that integrates a 5000-concept knowledge base and algorithms offering the possibility to ask questions and receive answers. The study aimed to shed more light on differences between three sub-types (intrinsic, germane and extraneous) of cognitive load and their relationship with learning gain, self-regulated learning and usability perception while students interacted with the AI-enriched book during an introductory biology course. We found that students displayed a beneficial learning pattern with germane cognitive load significantly higher than both intrinsic and extraneous loads showing that they were engaged in meaningful learning throughout the study. A significant correlation between germane load and accessing linked suggested questions available in the AI-book indicates that the book may support deep learning. Additionally, results showed that perceived non-optimal design, which deflects cognitive resources away from meaningful processing accompanied lower learning gains. Nevertheless, students reported substantially more favourable than unfavourable opinions of the AI-book. The findings provide new approaches for investigating cognitive load types in relation to learning with emerging digital tools in higher education. The findings also highlight the importance of optimally aligning educational technologies and human cognitive architecture.

  DOI

• Aging disrupts circadian gene regulation and function in macrophages

  Nature Immunology · 2021 · 159 citations

  • Biology
  • Cell biology
  • Immunology
  DOI

• Engaging With Biology by Asking Questions: Investigating Students’ Interaction and Learning With an Artificial Intelligence-Enriched Textbook

  Journal of Educational Computing Research · 2020 · 63 citations

  Senior authorCorresponding
  • Computer Science
  • Mathematics education
  • Psychology

  Applying artificial intelligence (AI) to support science learning is a prominent aspect of the digital education revolution. This study investigates students’ interaction and learning with an AI book, which enables the inputting of questions and receiving of suggested questions to understand biology, in comparison with a traditional E-book. Students ( n = 16) in a tertiary biology course engaged with the topics of energy in cells and cell signaling. The AI book group ( n = 6) interacted with the AI book first followed by the E-book, while the E-book group ( n = 10) did so in reverse. Students responded to pre-/posttests and to cognitive load, motivation, and usability questionnaires; and three students were interviewed. All interactions with the books were automatically logged. Results revealed a learning gain and a similar pattern of feature use across both books. Nevertheless, asking questions with the AI book was associated with higher retention and correlated positively with viewing visual representations more often. Students with a higher intrinsic motivation to know and to experience stimulation perceived book usability more favorably. Interviews revealed that posing and receiving suggested questions was helpful, while ideas for future development included more personalized feedback. Future research shall explore how learning can be benefitted with the AI-enriched book.

  DOI

Recent grants

• NIH Grant R01HL064148

  NIH · $1.4M · 2004

• How social behavior changes the brain

  NIH · $4.4M · 1989–2019

• NIH Grant R01HD037315

  NIH · $1.5M · 2004

• NIH Grant R01MH067752

  NIH · $1.6M · 2009

• NIH Grant P50HD029732

  NIH · $3.3M · 1998

Frequent coauthors

• David Sadava

  Beckman Research Institute

  61 shared

• Sally D. Hacker

  Oregon State University

  59 shared

• Jürgen Markl

  Johannes Gutenberg University Mainz

  58 shared

• Norman F. Ruby

  Stanford University

  43 shared

• David M. Hillis

  The University of Texas at Austin

  43 shared

• Thomas S. Kilduff

  42 shared

• Dennis A. Grahn

  36 shared

• Vinh H. Cao

  CAO Group (United States)

  30 shared

Education

• Ph.D., Biological Sciences

  Stanford University

  1984

• B.A., Zoology

  University of California, Berkeley

  1979

Similar researchers at Stanford University

• Yi Cuih-287
• Zhenan Baoh-224
• Karl Deisserothh-218
• Michael Snyderh-215
• Roger Blandfordh-205