Educational Research Association (AERA) conference held in Denver, Colorado.The SIG TICL focuses on theoretical foundations, fundamental research, and technical advances at the intersection of technology, instruction, cognition, and learning.Drawing from these areas, the eight studies featured in this issue examine how emerging technologies and pedagogical designs are reshaping learning experiences, learner engagement, and instructional practices across K-12 and higher education contexts, highlighting the interplay between technology, knowledge construction, and learning processes within digitally mediated environments.These studies address critical areas, including artificial intelligence (AI) and data-driven personalization, technologysupported pedagogical design, cognition and metacognition, and design and equity-oriented perspectives.Together, they advance our understanding of how thoughtfully designed and implemented digital technologies can support adaptive, inclusive, and meaningful learning, while also raising important questions related to ethics, equity, and instructional responsibility in technology-rich educational environments.Serving as a bridge between conference scholarship and academic publishing, this special issue extends the reach of these contributions by sharing timely research insights with a broader scholarly audience.

Abstract Applying design thinking to the development of sustainability e-learning can help universities offer digital experiences that align with contemporary student preferences. This study explores such premise in three European Higher Education Institutes (HEIs) based in Poland, Germany and Romania. First, 45 interviews with students from the Generation Z cohort (born between 1995 and 2010) were conducted and coded into end-user personas. Next, multidisciplinary teaching teams used those personas in a design-thinking workshop to prototype digital learning activities on sustainability. In this article, we report in details a replicable five-stage research protocol for gathering persona data and a seven-stage instructional design framework that translates such persona insights into e-learning deliverables. Results show that when instructors base their ideation and prototypes on persona-based empathy maps, the education activities are more relevant in context, more interactive, and better aligned with the United Nations Sustainable Development Goal of Quality Education (SDG 4). The contributions of this study are a detailed, evidence-based workflow that can be adapted by any higher education institution, a practical template for persona creation, conceptualisation, and rapid prototyping, and an analysis of the institutional enablers required to scale the approach.

Abstract Digital health interventions (DHIs) show promise for delivering effective mental health interventions at scale, however significant issues with sustained engagement have been observed. Current efforts to understand, predict, and prevent poor engagement have yielded little success due to the insufficient consideration of how engagement, and the factors driving it, can fluctuate in intensity across the user journey. One under-explored but highly relevant theoretical framework that can be applied in this context is self-regulated learning (SRL), due to the core emphasis on self-guided psychoeducation in digital mental health. SRL refers to the ability to regulate one’s emotions, cognitions, and behaviours to enhance learning outcomes. To investigate the utility and potential of this framework for advancing our knowledge of engagement with DHIs, we conducted semi-structured interviews with digital health users (n = 14) and explored the role of common SRL variables (i.e., drivers) in shaping engagement across the user journey. A template analysis identified four major themes of drivers: (i) motivational beliefs, feelings, and strategies, (ii) metacognitive feedback and control, (iii) emotional experiences, and (iv) physical and social environment. Participants broadly agreed that higher levels across one or more SRL drivers would enhance engagement behaviours, and that the importance of different drivers for shaping engagement can vary temporally. These preliminary findings highlight the relevance of SRL constructs in describing, understanding, and predicting engagement with DHIs, offering insight into potential opportunities for targeted intervention across the user journey to promote engagement. However, further research to quantitatively assess the relationships between SRL drivers and various metrics of engagement in a DHI trial is needed.

Abstract Assistive robotics holds promise for supporting therapeutic and educational interventions for autistic children, yet key questions remain regarding its practical benefits, limitations, and ethical implications. This study critically examines a 4-month therapeutic laboratory in which a humanoid robot (Pepper) was integrated into weekly sessions with four autistic children (ages 11–14, level 1 support needs). Through a mixed-methods approach, combining behavioural observations, standardised questionnaires, and thematic analysis of video recordings, we evaluated the robot’s role in fostering autonomy, communication, and engagement. Quantitative results showed limited improvements in functional autonomy, while qualitative findings highlighted the robot’s potential to stimulate curiosity, collaborative behaviours, and social participation, especially through unexpected modalities such as internet meme references. Challenges emerged around conversational rigidity, limited adaptability, and user frustration due to breakdowns in feedback and interaction flow. The study underscores the value of participatory, co-designed robotics in therapeutic contexts and offers design-oriented recommendations for developing more responsive, inclusive, and culturally adapted robot behaviours in child-centered interventions.

Abstract Most STEM classrooms overlook the intrinsic conceptual structure of domain content, strategies for improving students’ conceptual structure have promise for improving STEM learning outcomes. This experimental investigation continues the development of the web-based tool Graphical Interface of Knowledge Structure (GIKS) that provides immediate formative feedback as a network of concepts in the student’s essays alongside an expert referent network for comparison and reflection. What should this feedback network look like, especially, should it be more inclusive or small and focused? And is preexisting domain knowledge important for type of network feedback effectiveness? Undergraduate students in a second year Architecture Engineering course, after completing a 2-weeks long lesson on Building with Wood, were randomly assigned to a summary writing task with either Full feedback (a network with 14 central and 12 peripheral terms) or Focused feedback (a network with only the 14 central terms), and then immediately completed a knowledge structure survey. Two weeks later, they completed an End-of-Unit posttest that consisted of a Central-items and a Peripheral-items subtests. A significant interaction of feedback and domain knowledge was observed for post knowledge structure, the low domain knowledge students in the Focus feedback group had the most central link-agreement with the expert and the least peripheral links agreement. On the End-of-Unit declarative knowledge posttest, there was no difference for the Full or Focused feedback interventions, but the high domain knowledge students in both interventions performed significantly better than the low domain knowledge students on the central-items subtest but not on the peripheral-items subtest. This investigation shows the need for further research on the role of domain-normative central concepts and pragmatically contributes to the design of essay prompts for STEM classroom use.

Abstract Learning to read is an essential part of children’s overall development, and crucial in engaging them in our knowledge society. In this study, we investigated the effectiveness of two feedback conditions—explicit and implicit—on reading accuracy among first-grade pupils using our Automatic Speech Recognition (ASR) based Dutch reading tutor. Data were collected from 525 participants across 44 schools, with 263 pupils who received explicit feedback and 262 receiving implicit feedback. Mixed linear regression analyses revealed that both feedback conditions returned significant positive effects on reading accuracy in general, but, additionally, explicit feedback was more beneficial for accuracy gains in reading sentences. A trade-off between accuracy and reading speed was also observed, with improvements in accuracy often accompanied by slower reading speed. These findings suggest that explicit feedback may be more beneficial in early reading interventions.