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

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.Funding agencies: Marcus and Amalia Wallenberg Foundation [MAW 2014.0107]</p

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

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.Funding: Marcus and Amalia Wallenberg Foundation [MAW 2014.0107]</p

What does your gaze reveal about you? On the privacy implications of eye tracking

Technologies to measure gaze direction and pupil reactivity have become efficient, cheap, and compact and are finding increasing use in many fields, including gaming, marketing, driver safety, military, and healthcare. Besides offering numerous useful applications, the rapidly expanding technology raises serious privacy concerns. Through the lens of advanced data analytics, gaze patterns can reveal much more information than a user wishes and expects to give away. Drawing from a broad range of scientific disciplines, this paper provides a structured overview of personal data that can be inferred from recorded eye activities. Our analysis of the literature shows that eye tracking data may implicitly contain information about a user's biometric identity, gender, age, ethnicity, body weight, personality traits, drug consumption habits, emotional state, skills and abilities, fears, interests, and sexual preferences. Certain eye tracking measures may even reveal specific cognitive processes and can be used to diagnose various physical and mental health conditions. By portraying the richness and sensitivity of gaze data, this paper provides an important basis for consumer education, privacy impact assessments, and further research into the societal implications of eye tracking