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.

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals “shared language” among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants’ lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities.

Building on a foundation of innovative Machine Learning and Artificial Intelligence (AI) applications, this study presents an evolved approach to assessing student class participation (CP) and providing timely feedback in Masters in Business Administration (MBA) programs. This approach has been rigorously applied and analyzed over the course of four academic terms, thereby offering a substantive examination of its effectiveness. The methodological framework of this study is predicated on the utilization of AI transcription tools, such as GRAIN, to convert Zoom session recordings into analyzable text data. This has enabled a consistent and objective evaluation of each student's contributions during class discussions across four subsequent terms. The implementation involved MBA students who shared similar professional backgrounds and demographics, ensuring a consistent context for analysis. AI-generated insights were integrated with a dynamic dashboard, presenting an innovative way to track and assess CP. These insights underwent meticulous annotation and review to guarantee adherence to the educational standards and fairness expected by the institution. The AI's role as an initial assessor was balanced by the professor's final judgement, forming a complementary relationship that enhanced the educational process. This study's findings are particularly noteworthy in the context of teaching quality and effectiveness. Over the four terms of implementation, student evaluations reflected high satisfaction with respect to fairness and respect in grading, the timeliness of feedback, and the adherence to established grading criteria. Notably, the ratings for these specific criteria consistently exceeded the term mean under the category of "Evaluation." This suggests that the AI-augmented approach not only meets but surpasses traditional evaluation methods, aligning with the broader institutional goals of equitable and efficient educational assessment. In synthesizing the outcomes of this extended application, the paper delineates a clear trajectory of improvement in teaching effectiveness as perceived by the students, attributed to the responsible integration of AI in evaluative practices. These findings provide a compelling argument for the continued and expanded use of AI in the pedagogical process, with the potential to redefine the landscape of assessment in professional education.