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Caring assessments is an assessment design framework that considers the learner as a whole and can be used to design assessment opportunities that learners find engaging and appropriate for demonstrating what they know and can do. This framework considers learners’ cognitive, meta-cognitive, intra-and inter-personal skills, aspects of the learning context, and cultural and linguistic backgrounds as ways to adapt assessments. Extending previous work on intelligent tutoring systems that “care” from the field of artificial intelligence in education (AIEd), this framework can inform research and development of personalized and socioculturally responsive assessments that support students’ needs. In this article, we (a) describe the caring assessment framework and its unique contributions to the field, (b) summarize current and emerging research on caring assessments related to students’ emotions, individual differences, and cultural contexts, and (c) discuss challenges and opportunities for future research on caring assessments in the service of developing and implementing personalized and socioculturally responsive interactive digital assessments. 

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs. 

Learner models are used to support the implementation of personalization features in Adaptive Instructional Systems (AISs; e.g., adaptive sequencing of activities, adaptive feedback), which are important aspects of Intelligent Adaptive Systems. With the increased computational power, more advanced methodologies, and more available data, learner models include a variety of Artificial Intelligence techniques. These techniques have different levels of complexity, which influence interpretability and explainability of learner models. Interpretable and explainable learner models can facilitate appropriate use of the learner modeling information in AISs, their adoption, and scalability. This chapter elaborates on the definitions of interpretability and explainability, describes interpretability and explainability levels of different models, elaborates on the levels of explainability to produce needed information for teachers and learners, and discusses implications and future work in this area. 

This paper explores the differences between two types of natural language conversations between a student and pedagogical agent(s). Both types of conversations were created for formative assessment purposes. The first type is conversation-based assessment created via knowledge engineering which requires a large amount of human effort. The second type, which is less costly to produce, uses prompt engineering for LLMs based on Evidence-Centered design to create these conversations and glean evidence about students¿½f knowledge, skills and abilities. The current work compares linguistic features of the artificial agent(s) discourse moves in natural language conversations created by the two methodologies. Results indicate that more complex conversations are created by the prompt engineering method which may be more adaptive than the knowledge engineering approach. However, the affordances of prompt engi-neered, LLM generated conversation-based assessment may create more challenges for scoring than the original knowledge engineered conversations. Limitations and implications are dis-cussed.