Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has some potential to increase the assessment’s validity and reliability. The primary objective of this study is to examine the feasibility of a novel approach for stealthily assessing literacy skills using games in an intelligent tutoring system (ITS) designed for reading comprehension strategy training. We investigated the degree to which learners’ game performance and enjoyment predicted their scores on standardized reading tests. Amazon Mechanical Turk participants (n = 211) played three games in iSTART and self-reported their level of game enjoyment after each game. Participants also completed the Gates–MacGinitie Reading Test (GMRT), which includes vocabulary knowledge and reading comprehension measures. The results indicated that participants’ performance in each game as well as the combined performance across all three games predicted their literacy skills. However, the relations between game enjoyment and literacy skills varied across games. These findings suggest the potential of leveraging serious games to assess students’ literacy skills and improve the adaptivity of game-based learning environments.

Writing is essential for success in academics and everyday tasks, but the development of writing skills depends on consistent access to high-quality instruction, extended practice, and personalized feedback. To address these demands and meet students’ needs, educators and researchers have turned to technology-based writing tools. Ideally, these tools integrate the core components of intelligent tutoring, including a domain model, student model, tutor model, and interface model to engage students with individualized feedback that is linked to adaptive writing instruction. However, the landscape of writing tools still has much room for improvement in terms of incorporating advanced artificial intelligence-enabled features to better approximate intelligent tutoring systems (ITSs). This chapter describes the key elements of ITS technologies and how they can be integrated to further develop ITS tools for writing. To this end, this chapter (1) summarizes evidence-based aspects of successful ITSs and how they might be integrated into computer-based tools for writing, (2) reviews how existing systems have leveraged intelligent tutoring approaches, and (3) articulates how future technology-based writing tools could implement advanced intelligent tutoring features to better meet students’ needs. The chapter concludes with the implications and future directions of intelligent tutoring for the teaching and learning of writing.

Recent advances in large language models (LLMs) have made automated multiple-choice question (MCQ) generation increasingly feasible; however, reliably producing items that satisfy controlled cognitive demands remains a challenge. To address this gap, we introduce ReQUESTA, a hybrid, multi-agent framework for generating cognitively diverse MCQs that systematically target text-based, inferential, and main idea comprehension. ReQUESTA decomposes MCQ authoring into specialized subtasks and coordinates LLM-powered agents with rule-based components to support planning, controlled generation, iterative evaluation, and post-processing. We evaluated the framework in a large-scale reading comprehension study using academic expository texts, comparing ReQUESTA-generated MCQs with those produced by a single-pass GPT-5 zero-shot baseline. Psychometric analyses of learner responses assessed item difficulty and discrimination, while expert raters evaluated question quality across multiple dimensions, including topic relevance and distractor quality. Results showed that ReQUESTA-generated items were consistently more challenging, more discriminative, and more strongly aligned with overall reading comprehension performance. Expert evaluations further indicated stronger alignment with central concepts and superior distractor linguistic consistency and semantic plausibility, particularly for inferential questions. These findings demonstrate that hybrid, agentic orchestration can systematically improve the reliability and controllability of LLM-based generation, highlighting workflow design as a key lever for structured artifact generation beyond single-pass prompting.

Reading comprehension is essential for students' ability to build knowledge. Students' comprehension abilities can be enhanced by providing students with deliberate practice and formative feedback on reading comprehension strategies. iSTART is an Intelligent Tutoring System (ITS) that is designed to provide instruction in reading strategies with minimal teacher supervision - affording the ability to teach reading strategies at scale. In the current study, undergraduate students received reading strategy instruction and opportunities for deliberate practice via the iSTART intelligent tutoring system or not (i.e., no-treatment control group). Participants' reading comprehension and psychology knowledge were assessed. The iSTART group demonstrated substantially greater scores than the control group on a post-training reading comprehension measure (Cohen's d > 1.0). The average psychology knowledge scores did not differ between iSTART (post-training) and control groups, but overall these scores were unexpectedly low. Within the iSTART group, there was no difference in reading comprehension and knowledge scores as a function of students' different behaviors in the system. Overall, the results indicate that iSTART is an effective tool to teach reading strategies at large scale. However, further work is required to test the extent to which iSTART supports knowledge building.