Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.

Human-centric systems (HCSs), e.g. , used in healthcare facilities [ Given the long testing horizon ( e.g. , several years, or semesters, in healthcare, and IE, respectively) and the high cost of recruiting participants, online testing is considered exceedingly The work was done at North Carolina State University. In this section, we introduce the FPS method, which determines the policy to be deployed to new participants that join an existing cohort, conditioned only on their initial states.

In the aftermath of COVID-19, many universities implemented supplementary "reinforcement" roles to support students in demanding courses. Although the name for such roles may differ between institutions, the underlying idea of providing structured supplementary support is common. However, these roles were often poorly defined, lacking structured materials, pedagogical oversight, and integration with the core teaching team. This paper reports on the redesign of reinforcement sessions in a challenging undergraduate course on formal methods and computational models, using a large language model (LLM) as a reflective planning tool. The LLM was prompted to simulate the perspective of a second-year student, enabling the identification of conceptual bottlenecks, gaps in intuition, and likely reasoning breakdowns before classroom delivery. These insights informed a structured, repeatable session format combining targeted review, collaborative examples, independent student work, and guided walkthroughs. Conducted over a single semester, the intervention received positive student feedback, indicating increased confidence, reduced anxiety, and improved clarity, particularly in abstract topics such as the pumping lemma and formal language expressive power comparisons. The findings suggest that reflective, instructor-facing use of LLMs can enhance pedagogical design in theoretically dense domains and may be adaptable to other cognitively demanding computer science courses.

Human-centric systems (HCSs), e.g. , used in healthcare facilities [ Given the long testing horizon ( e.g. , several years, or semesters, in healthcare, and IE, respectively) and the high cost of recruiting participants, online testing is considered exceedingly The work was done at North Carolina State University. In this section, we introduce the FPS method, which determines the policy to be deployed to new participants that join an existing cohort, conditioned only on their initial states.

Reasoning is essential for closed-domain QA systems in which procedural correctness and policy compliance are critical. While large language models (LLMs) have shown strong performance on many reasoning tasks, recent work reveals that their reasoning traces are often unfaithful - serving more as plausible justifications than as causally grounded derivations. Efforts to combine LLMs with symbolic engines (e.g., Prover9, Z3) have improved reliability but remain limited to static forms of logic, struggling with dynamic, state-based reasoning such as multi-step progressions and conditional transitions. In this paper, we propose MCFR (Model Checking for Formal Reasoning), a neuro-symbolic framework that integrates LLMs with model checking to support property verification. MCFR translates natural language into formal specifications and verifies them over transition models. To support evaluation, we introduce EduMC-QA, a benchmark dataset grounded in real academic procedures. Our results show that MCFR improves reasoning faithfulness and interpretability, offering a viable path toward verifiable QA in high-stakes closed-domain applications. In addition to evaluating MCFR, we compare its performance with state-of-the-art LLMs such as ChatGPT, DeepSeek, and Claude to contextualize its effectiveness.

This paper presents a personalized lecture concept using educational blocks and its demonstrative application in a new university lecture. Higher education faces daily challenges: deep and specialized knowledge is available from everywhere and accessible to almost everyone. University lecturers of specialized master courses confront the problem that their lectures are either too boring or too complex for the attending students. Additionally, curricula are changing more rapidly than they have in the past 10-30 years. The German education system comprises different educational forms, with universities providing less practical content. Consequently, many university students do not obtain the practical skills they should ideally gain through university lectures. Therefore, in this work, a new lecture concept is proposed based on the extension of the just-in-time teaching paradigm: Personalized and Demand-Based Education. This concept includes: 1) an initial assessment of students' backgrounds, 2) selecting the appropriate educational blocks, and 3) collecting ongoing feedback during the semester. The feedback was gathered via Pingo, ensuring anonymity for the students. Our concept was exemplarily tested in the new lecture "Practical Tools for Control Engineers" at the Karlsruhe Institute of Technology. The initial results indicate that our proposed concept could be beneficial in addressing the current challenges in higher education.

--Students disengaging from their tasks can have serious long-term consequences, including academic drop-out. This is particularly relevant for students in distance education. One way to measure the level of disengagement in distance education is to observe participation in non-mandatory exercises in different online courses. In this paper, we detect student disengagement in the non-mandatory quizzes of 42 courses in four semesters from a distance-based university. We carefully identified the most informative student log data that could be extracted and processed from Moodle. Then, eight machine learning algorithms were trained and compared to obtain the highest possible prediction accuracy. Using the SHAP method, we developed an explainable machine learning framework that allows practitioners to better understand the decisions of the trained algorithm. The experimental results show a balanced accuracy of 91%, where about 85% of disengaged students were correctly detected. On top of the highly predictive performance and explainable framework, we provide a discussion on how to design a timely intervention to minimise disengagement from voluntary tasks in online learning. HE advent of distance education has made learning more flexible than ever before. Instead of having to attend classes and solve tasks at specific time, students are granted more freedom in choosing when to engage with their academic workload. This flexibility attracts many non-traditional student groups to higher education, including students that are employed outside of their studies, either fully or part-time. While deadlines are still set in place, students are responsible themselves for planning and time management, especially as far as non-mandatory tasks and exercises are concerned. This freedom can also lead to satisficing behaviour, meaning students only do the bare minimum to pass their courses (see e.g., [1], [2]). Bergamin are with the Institute for Research in Open-, Distance-and eLearning, Swiss Distance University of Applied Sciences, Brig, CH-3900, Switzerland (e-mail addresses: behnam.parsaeifard@ffhs.ch, N. Bergamin (e-mail address: nicole.bergamin@ffhs.ch) is with Department of Informatics, Swiss Distance University of Applied Sciences, Brig, CH-3900, Switzerland. Bergamin is also with the North-West University, Potchefstroom, 2531, South Africa. The COVID-19 pandemic is thought to have fostered this kind of behaviour even more [4]. Non-completion of voluntary tasks, such as optional quizzes, is a form of behavioural disengagement strongly linked to academic drop-out or attrition [5]-[8].

This essay was adapted from Phil Christman's newsletter, the Tourist. Before 2023, my teaching year used to follow a predictable emotional arc. In September, I was always excited, not only about meeting a new crop of first-year writing students but even about the prep work. My lesson-planning sessions would take longer than intended and yet leave me feeling energized. I'd look forward to conference week--the one-on-one meetings I try to hold with every student, every term, at least once--and even to the first stack of papers.