Advancements in artificial intelligence are rapidly increasing. The new‐generation large language models, such as ChatGPT and GPT‐4, bear the potential to transform educational approaches, such as peer‐feedback. To investigate peer‐feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross‐disciplinary framework that aims to facilitate the development of NLP‐based adaptive measures for supporting peer‐feedback processes in digital learning environments. To conceptualize this process, we introduce a peer‐feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer‐feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer‐feedback process model to exemplify a range of NLP‐based adaptive support measures. We also discuss the current challenges and suggest directions for future cross‐disciplinary research on the effectiveness and other dimensions of NLP‐based adaptive support for peer‐feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer‐feedback in digital learning environments. Practitioner notes: What is already known about this topic There is considerable research in educational science on peer‐feedback processes. Natural language processing facilitates the analysis of students' textual data. There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer‐feedback process. What this paper adds A comprehensive overview model that describes the relevant activities and products in the peer‐feedback process. A terminological and procedural scheme for designing NLP‐based adaptive support measures. An application of this scheme to the peer‐feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer‐feedback. Implications for practice and/or policy To boost the effectiveness of their peer‐feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings. Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.

Medical knowledge encompasses both conceptual (facts or “what” information) and procedural knowledge (“how” and “why” information). Conceptual knowledge is known to be an essential prerequisite for clinical problem solving. Primarily, medical students learn from textbooks and often struggle with the process of applying their conceptual knowledge to clinical problems. Recent studies address the question of how to foster the acquisition of procedural knowledge and its application in medical education. However, little is known about the factors which predict performance in procedural knowledge tasks. Which additional factors of the learner predict performance in procedural knowledge?

Recently, the option to use large language models as a middleware connecting various AI tools and other large language models led to the development of so-called large multimodal foundation models, which have the power to process spoken text, music, images and videos. In this overview, we explain a new set of opportunities and challenges that arise from the integration of large multimodal foundation models in education.

The purpose of our study was the development and validation of a modified electronic key feature exam of clinical decision-making skills for undergraduate medical students. Therefore, the reliability of the test (15 items), the item difficulty level, the item-total correlations and correlations to other measures of knowledge (40 item MC-test and 580 items of German MC-National Licensing Exam, Part II) were calculated. Based on the guidelines provided by the Medical Council of Canada, a modified electronic key feature exam for internal medicine consisting of 15 key features (KFs) was developed for fifth year German medical students. Long menu (LM) and short menu (SM) question formats were used. Acceptance was assessed through a questionnaire. Thirty-seven students from four medical schools voluntarily participated in the study. The reliability of the key feature exam was 0.65 (Cronbach's alpha). The items' difficulty level scores were between 0.3 and 0.8 and the item-total correlations between 0.0 and 0.4. Correlations between the results of the KF exam and the other measures of knowledge were intermediate (r between 0.44 and 0.47) as well as the learners' level of acceptance. The modified electronic KF examination is a feasible and reliable evaluation tool that may be implemented for the assessment of clinical undergraduate training.

Professional knowledge is highlighted as an important prerequisite of both medical doctors and teachers. Based on recent conceptions of professional knowledge in these fields, knowledge can be differentiated within several aspects. However, these knowledge aspects are currently conceptualized differently across different domains and projects. Thus, this paper describes recent frameworks for professional knowledge in medical and educational sciences, which are then integrated into an interdisciplinary two-dimensional model of professional knowledge that can help to align terminology in both domains and compare research results. The models’ two dimensions differentiate between cognitive types of knowledge and content-related knowledge facets and introduces a terminology for all emerging knowledge aspects. The models’ applicability for medical and educational sciences is demonstrated in the context of diagnosis by describing prototypical diagnostic settings for medical doctors as well as for teachers, which illustrate how the framework can be applied and operationalized in these areas. Subsequently, the role of the different knowledge aspects for acting and the possibility of transfer between different content areas are discussed. In conclusion, a possible extension of the model along a “third dimension” that focuses on the effects of growing expertise on professional knowledge over time is proposed and issues for further research are outlined.