Amber Simpson

Background: Given the growing need for a well‐prepared engineering workforce, strategic investments must extend beyond formal education to include nonformal learning environments. While prior research highlights the role of parents, educators, and mentors in shaping young children's engineering development, siblings remain an understudied yet influential force. Purpose: This study examined older siblings' roles when interacting with their younger siblings on engineering design tasks within two nonformal learning environments—home and an afterschool program. These interactions occurred during naturalistic environments, void of adult guidance and presence. Methods: Using a naturalistic exploratory approach, we examined the interactions of six sibling pairs. We collected and analyzed approximately 13 h of video across the two nonformal learning environments. Results: Siblings enact a variety of interaction strategies and roles that shape joint engagement in engineering practices differently. While some roles overlapped with those in the literature, we observed roles unique to siblings and young learners. The most frequent roles observed were colleague/peer, lead engineer, and provocateur. However, even rare roles and interaction strategies were found to support engineering practices. Conclusion: This study highlights the potential of sibling interactions as a meaningful component of engineering learning; integrating engineering activities for siblings could leverage natural social and learning relationships to support siblings' development and growth as engineers.

Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational Adaptation Multimodal) framework. This modular pipeline decomposes multimodal educational content into semantically tagged, pedagogically annotated units for regeneration into alternative formats while preserving source traceability. STREAM is designed to integrate automatic speech recognition, transformer-based natural language processing, and planned computer vision components to extract instructional elements from teacher explanations, slides, and embedded media. Each unit receives metadata, including time codes, instructional type, cognitive demand, and prerequisite concepts, designed to enable format-specific regeneration with explicit provenance links. For a predefined visual-learner profile, the system generates annotated path diagrams, two-panel instructional guides, and entity pictograms with complete back-link coverage. Ablation studies confirm that individual components contribute measurably to output completeness without compromising traceability. This paper reports results from a tightly scoped feasibility pilot that processes a single five-minute elementary STEM video offline under clean audio–visual conditions. We position the pilot’s limitations as testable hypotheses that require validation across diverse content domains, authentic deployments with ambient noise and bandwidth constraints, multiple learner profiles, including multilingual students and learners with disabilities, and controlled comprehension studies. The contribution is a transparent technical demonstration of feasibility and a methodological scaffold for investigating whether within-lesson content transformation can support personalized learning at scale.

The underrepresentation of girls and women in science, technology, engineering, and mathematics (STEM) fields is a persistent issue. One approach to encourage girls’ entry into STEM is by fostering their identity development as STEM individuals within out-of-school learning environments. Informed by research on recognition, we examined five middle school girls’ identity development as a STEM person in an afterschool program that integrated Western STEM concepts, practices, and processes with both archaeological and Indigenous concepts, practices, and processes as grounded in STEM disciplines. The results of this study highlight the complexity and variability in how identity as a STEM learner was experienced, authored, and positioned by the five girls. The results of this study further suggest that adolescent girls may shape their STEM identity more through how their actions and behaviors reflect what it means to embody a STEM person in a given learning environment than through their engagement with STEM concepts, practices, or processes. While the results add to our current knowledge regarding girls’ identity development in STEM, it also raised questions as to how societal, cultural, and program-level norms and expectations may be shaping middle school girls’ STEM identity development as productive or less productive, which may impact girls’ persistence in a STEM field and pursuit of career in a STEM field.

Most adaptive learning systems personalize around content sequencing and difficulty adjustment rather than transforming instructional material within the lesson itself. This paper presents the STREAM (Semantic Transformation and Real-Time Educational Adaptation Multimodal) framework. This modular pipeline decomposes multimodal educational content into semantically tagged, pedagogically annotated units for regeneration into alternative formats while preserving source traceability. STREAM integrates automatic speech recognition, transformer-based natural language processing, and planned computer vision components to extract instructional elements from teacher explanations, slides, and embedded media. Each unit receives metadata including timecodes, instructional type, cognitive demand, and prerequisite concepts, enabling format-specific regeneration with explicit provenance links. We report results from a tightly scoped feasibility pilot processing a single five-minute elementary STEM video offline under clean audio-visual conditions. For a predefined visual-learner profile, the system generates annotated path diagrams, two-panel instructional guides, and entity pictograms with complete back-link coverage. Ablation studies confirm individual components contribute measurably to output completeness without compromising traceability. This narrow scope precludes claims about classroom effectiveness, real-time streaming, scalability across content types, or educational impact across learner populations. We position these as testable hypotheses requiring validation across diverse content domains, authentic deployments with ambient noise and bandwidth constraints, multiple learner profiles including multilingual students and learners with disabilities, and controlled comprehension studies. The contribution is a transparent technical demonstration and methodological scaffold for investigating whether within-lesson content transformation can meaningfully support personalized learning at scale.