Adaptive Experimentation is one of the most promising approaches to support complex decision-making in learning experience design and delivery. This paper reports on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Digital Learning Challenge framework, and summarizes data-driven lessons learned and best practices for Adaptive Experimentation in education. We outline key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project, focusing on implications relevant to platform developers, researchers, practitioners, and policy stakeholders to integrate Adaptive Experiments in real-world courses.

Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper’s hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document’s navigational structure. Drawing insights from a formative study on academic reading practices, we introduce Treereader, a novel language model-augmented paper reader. Treereader decomposes papers into an interactive tree structure where each section is initially represented by an LLM-generated concise summary, with underlying details accessible on demand. This design allows users to quickly grasp core ideas, selectively explore sections of interest, and verify summaries against the source text. A user study was conducted to evaluate Treereader’s impact on reading efficiency and comprehension. Treereader provides a more focused and efficient way to navigate and understand complex academic literature by bridging hierarchical summarization with interactive exploration.

This research evaluates voice-based self-explanations as a pedagogical tool in preparation for lectures, assesses user preferences between voice and text, and derives design insights. We report two studies: Study 1, a quasi-experimental field study, with 247 participants divided into voice-based (N = 83), text-based (N = 81), and choice (N = 83) conditions. Study 2 uses semi-structured interviews (N = 16) to explore perceptions of the interaction paradigms in-depth. Results from the first study revealed a general preference for text, though voice users produced longer responses and more topic-related keywords. Over time, the preference for voice increased among students, from 10% to 46%, when given a choice. Study 2 suggested that factors like social presence contribute to hesitance toward voice-based explanations, with a cognitive load, self-confidence, and performance anxiety also influencing medium preferences. Our findings highlight design recommendations and demonstrate the potential of voice-based self-explanations in educational settings, indicating that mixed interfaces might better meet diverse needs.

Generating self-explanations has been identified as a successful strategy in helping learners engage with course content and organize what they learn in a structured format. While typing an explanation may allow more structure and formality, explaining by voice can be more natural and help free cognitive resources to focus on learning goals and understanding concepts. As we investigated the effects and students' perceptions of using voice or text to self-explain new course concepts, we failed to find a tool that would meet our needs. We present our work in designing and developing VoiceEx, a submission courseware that allows text and voice input to collect data in both mediums. VoiceEx was created to support a self-explanations intervention for computer science students; however, given its features and the advantages of being able to collect spoken responses, it can be used in a variety of environments. Future refinement of this tool includes artificial intelligence features to better guide students' submissions.

Research has revealed the positive effects of reflection on helping students manage their psychological well-being. Hence, we are motivated to investigate the design space of how we can communicate the values of doing reflections. We limit our work to voice reflection because of its inclusivity and effectiveness. We designed a pilot survey on Qualtrics to collect qualitative responses and integrated voice recording features from Phonic.ai. Participants were presented with 4 sample voice recordings related to college students' daily lives and asked to complete a simple voice reflection activity based on the samples they listened to. Then, they were asked to provide feedback on these examples. We deployed the survey on Amazon Mechanical Turk (MTurk) and collected 221 effective responses. By conducting thematic analysis, we found several insightful themes: emotional speech, diverse content, and clear structure are important elements to include, while examples should avoid being overly scripted. The findings suggest ways to design effective examples to engage students in voice reflections and open up the possibilities for further investigations into the design features of voice reflection platforms.

With the widespread usage of mobile devices, users can now choose to provide input through voice or text. As researchers frequently ask students open-ended questions, we want to explore a natural mode to obtain better feedback in surveys. This study details a preliminary study demonstrating the importance of allowing students to choose between voice or text input to respond to surveys. A survey with several open-ended questions was deployed in a CS1 course. Correlations between the gender of the respondent and their method of responding were evaluated. We found that voice responses tended to be longer and preferred more by females relative to male students.