Assessment of Augmented Reality Labs for Biology Education Outcomes

Thesis Abstract

Augmented reality (AR) technology is increasingly integrated into biology education to enhance conceptual understanding, procedural fluency, and scientific inquiry skills; however, robust evidence on its effectiveness across diverse learner populations remains inconsistent, with concerns about cognitive load, equity of access, and long-term retention. This study addresses the problem by examining how AR lab simulations influence biology students’ learning outcomes, engagement, and practical competencies in comparison with conventional wet-lab instruction. The aim is to determine whether AR-enabled laboratories can produce superior or equivalent educational gains while improving student motivation and procedural accuracy. Specific objectives are to (i) evaluate the impact of AR labs on achievement in core biology topics (e.g., cellular respiration, genetics, and microscopy), (ii) assess changes in procedural knowledge, science inquiry skills, and laboratory safety behaviors, (iii) investigate student engagement, motivation, and perceived cognitive load, and (iv) explore equity considerations related to access and usability across different socioeconomic groups. The study adopts a quasi-experimental, mixed-methods design underpinned by constructivist and situated cognition theories, drawing on the Technology Acceptance Model to interpret adoption patterns. The population comprises undergraduate biology majors enrolled in two mid-sized universities during a single academic term. A total of 420 participants will be recruited, with 210 assigned to the AR intervention group and 210 to the traditional laboratory control group through matched-pair assignment by prior biology GPA and gender. Data collection instruments include standardized biology achievement tests administered as pre- and post-tests, validated scales for laboratory skills confidence and intrinsic motivation, task-specific rubrics for procedural competence, and cognitive load measurement using the NASA-TLX instrument. Lab performance will be assessed through practical exams and rubric-based observation checklists during both AR-based and conventional lab sessions. Additional qualitative data will be gathered via semi-structured interviews with a subsample of 40 students and focus groups with 8 laboratory instructors to triangulate findings. Instrument validity and reliability will be established through pilot testing (n=60) and Cronbach’s alpha analysis (target ? ? .80) for survey measures; inter-rater reliability for performance rubrics will be assessed using Cohen’s kappa (target > .70). Quantitative data will be analyzed using ANCOVA to compare post-test achievement between groups while controlling for pre-test scores, and MANOVA to examine multiple dependent variables related to skills and motivation. Regression analyses will identify predictors of AR lab effectiveness, including cognitive load and perceived ease of use. Qualitative data will be analyzed thematically using Braun and Clarke’s approach to identify patterns related to engagement, usability, and instructional design. A convergent parallel design will integrate quantitative and qualitative findings, with a meta-inference highlighting contexts in which AR labs yield the greatest benefits or pose challenges. Expected findings include (1) AR labs will yield statistically significant improvements in conceptual biology achievement and procedural competence compared with traditional labs, with moderate effect sizes (Cohen’s d ? 0.40–0.60); (2) higher engagement and intrinsic motivation in the AR group, moderated by lower perceived cognitive load when AR interfaces are well-designed; (3) enhanced transfer of microscopic and cellular concepts to novel tasks, evidenced by higher performance in off-lab problem-solving activities; and (4) mixed equity outcomes, with access disparities partially mitigated by institutional provision of devices and inclusive interface design. The study contributes to knowledge by providing rigorous, empirically grounded evidence on AR lab effectiveness in higher education biology, clarifying how AR affects cognitive load, motivation, and skill development, and offering a differentiated understanding of context-specific benefits and limitations. Practical implications include evidence-based guidelines for AR instructional design, criteria for selecting target biology topics for AR simulation, and recommendations for scalable implementation in diverse institutional settings. The main conclusion is that strategically designed AR labs can complement traditional biology instruction to enhance learning outcomes and engagement, particularly for visually demanding topics, provided that challenges related to accessibility, instructor training, and measurement of complex competencies are addressed. Recommendations emphasize iterative design testing, hybrid lab models, and equity-focused deployment, with future research extending longitudinal assessments and exploring cross-disciplinary applications.