Reflection is essential for fostering metacognitive development. However, many learners struggle to engage in meaningful, structured reflection without further support. To support learners in reflective practices, we developed MindBuddy, a learner-centered tutor that guides students individually through reflective writing tasks and provides adaptive feedback. After an iterative user-centered development process (two pilot studies, n=81), we conducted a longitudinal field-experimental classroom study with n=34 undergraduates over a six-week period to compare two different tutoring modalities in MindBuddy (1) interactive conversational tutoring (TG1) with (2) constructive feedback-only (TG2). No significant differences in perceived skills were found, suggesting that the conversational interactions may enhance students’ confidence in their reflective abilities, similarly to adaptive feedback interaction. While differences were found for formal reflective structure, our findings suggest that conversational tutoring has the potential to increase learners’ engagement with reflective writing. Future research on whether such engagement translates into measurable performance gains is necessary.

Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as premature closure and over-reliance on heuristics, and they struggle to transfer diagnostic strategies to new cases. Scenario-based learning (SBL) enhanced by Learning Analytics (LA) and large language models (LLM) offers a promising approach by combining realistic case experiences with personalized scaffolding. Yet, how different scaffolding approaches shape reasoning processes remains insufficiently explored. This study introduces PharmaSim Switch, an SBL environment for pharmacy technician training, extended with an LA- and LLM-powered pharmacist agent that implements pedagogical conversations rooted in two theory-driven scaffolding approaches: structuring and problematizing, as well as a student learning trajectory. In a between-groups experiment, 63 vocational students completed a learning scenario, a near-transfer scenario, and a far-transfer scenario under one of the two scaffolding conditions. Results indicate that both scaffolding approaches were effective in supporting the use of diagnostic strategies. Performance outcomes were primarily influenced by scenario complexity rather than students’ prior knowledge or the scaffolding approach used. The structuring approach was associated with more accurate Active and Interactive participation, whereas problematizing elicited more Constructive engagement. These findings underscore the value of combining scaffolding approaches when designing LA- and LLM-based systems to effectively foster diagnostic reasoning.

Providing argumentation feedback is considered helpful for students preparing to work in collaborative environments, helping them with writing higher-quality argumentative texts. Domain-independent natural language processing (NLP) methods, such as generative models, can utilize learner errors and fallacies in argumentation learning to help students write better argumentative texts. To test this, we collect design requirements, and then design and implement two different versions of our system called ALure to improve the students' argumentation skills. We test how ALure helps students learn argumentation in a university lecture with 305 students and compare the learning gains of the two versions of ALure with a control group using video tutoring. We find and discuss the differences of learning gains in argument structure and fallacies in both groups after using ALure, as well as the control group. Our results shed light on the applicability of computer-supported systems using recent advances in NLP to help students in learning argumentation as a necessary skill for collaborative working settings.

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

Student peer review writing is prevalent and important in education for fostering critical thinking and learning motivation. However, it often entails challenges such as high effort and writer’s block. Leaving students unsupported may thus diminish the efficacy of the process. Large Language Models (LLMs) offer a potential remedy, but their utility hinges on user-centered design. Guided by design-determining constructs from the Cognitive Process Theory of Writing, we developed an intelligent writing support tool to alleviate these challenges, aiding 1) ideation and 2) evaluation. A randomized experiment (n=120) confirmed users were less inclined to utilize the tool’s intelligent features when offered pre-supplied ideas or evaluations, validating our approach. Moreover, students engaged not less but more with their writing if support was available, indicating an enhanced experience. Our research illuminates design choices for enhancing LLM-based tools’ usability and user experience, specifically optimizing intelligent writing support tools to facilitate student peer review.

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for many learners. Recent advances in machine learning (ML) have shown the potential to support learners in complex writing tasks. To test the effects of ML-based support on students’ legal writing skills, we developed the intelligent writing support system LegalWriter. We evaluated the system’s effectiveness with 62 students. We showed that students who received intelligent writing support based on their errors wrote more structured and persuasive case solutions with a better quality of legal writing than the current benchmark. At the same time, our results demonstrated the positive effects on the students’ writing processes.

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diverse effects on user behaviors when writing the review text. Generative language models might be able to support humans in formulating reviews with textual suggestions. Previous systems use two designs for providing text suggestions, but do not empirically evaluate them: inline and list of suggestions. To investigate the effects of embedding NLP text generation models in the two designs, we collected user requirements to implement Hamta as an example of assistants providing reviewers with text suggestions. Our experiment on comparing the two designs on 31 participants indicates that people using the inline interface provided longer reviews on average, while participants using the list of suggestions experienced more ease of use in using our tool. The results shed light on important design findings for embedding text generation models in user-centered assistants.

Computer-assisted collaborative peer grading is a developing growth area in academic evaluation. However, peer assessment often needs help with problems such as the lack of reliability, transparency, fairness, grading speed, and motivation to participate among students. The literature suggests several principles that each partly address the said issues. We propose a novel decentralized approach to academic peer assessment, using blockchain as an underlying technology, to address the principal problems in traditional peer assessment. We also derive design concepts for a modern courseware (CW) application consisting of our method and apply them to implement our approach in a CW called Blockment. We test the effectiveness of our method and system by running quantitative and qualitative experiments, proving our claims of improving reliability, transparency, fairness, grading speed, and motivation of grades in peer assessment. The results suggest embedding our method and system in academic courses to improve conventional peer grading methods.