Large language models (LLMs) have rapidly advanced across natural language processing tasks and are increasingly integrated into education for tutoring, assessment, and curriculum design. While closed-source systems have led early adoption, open-source LLMs are gaining attention for their transparency, adaptability, and institutional control, making them particularly relevant for responsible and equitable innovation in learning environments. However, systematic understanding of their educational use and impact remains limited. This paper presents a systematic review of open-source LLMs in education. We synthesize human-empirical studies to address three research questions concerning (i) how opensource LLMs are adopted across teaching and learning contexts, (ii) what learner outcomes and evidentiary bases are reported, and (iii) how teachers engage with these models through pedagogical and orchestration roles. By organizing and analyzing existing findings across these dimensions, the review establishes a foundational map of current practices, identifies gaps in empirical coverage and methodological rigor, and outlines directions for future research on reproducible and inclusive integration of open-source LLMs in education.

Writing is cognitively demanding and anxiety-provoking for English as a Foreign Language (EFL) learners, especially under time pressure. This paper presents The Brand War, a web-based gamified writing application combining competitive game mechanics with iterative GPT-4.1-powered formative feedback for undergraduate EFL learners completing a timed narrative writing task. Students role-play as marketing interns competing for a job offer, using review passes to receive AI feedback, attack opponents, or shield their own passes while drafting a 500-word brand story. We conducted an exploratory single-session classroom study with 29 university EFL students in Taiwan to examine engagement patterns, whether iterative AI feedback improved writing performance across revisions, and how AI and human scores related to overall outcomes. Students wrote within 60 minutes, using up to five AI feedback passes before a final human-graded submission. Most (65.5

To address these critical gaps, although many research studies imply that argument mapping can enhance writers' abilities in argumentation and critical thinking, we need to consolidate the available evidence as the first step to better understand the scope and coverage of these tools. Therefore, this paper adopts a scoping review methodology guided by the PRISMA-ScR framework. Through this systematic scoping approach, we aim to map the breadth of research on argument visualization and learning by identifying key themes and fields and highlighting gaps in empirical studies. By synthesizing existing evidence across disciplines and contexts, we hope that the scoping review will provide a foundation for future research directions, particularly in the areas of experimental validation and pedagogical design. This consolidation is especially timely given the rapid expansion of online and hybrid learning environments where such tools could provide crucial cognitive scaffolding.

The rapid rise of Artificial Intelligence (AI) tools prompts educators to revisit how learning theories inform instruction and assessment. While AI supports personalization, adaptive feedback, and automation, its alignment with established learning theories remains unclear. This study examines how AI tools engage cognitive, metacognitive, and affective dimensions using an open dataset of AI-powered educational tools. Using topic modeling techniques and cluster analysis, we identify thematic categories and their alignment with Bloom's Taxonomy, the Two-Level Model of Metacognition, and Control-Value Theory. Findings show a strong emphasis on lower-order processes (e.g., Remembering and Understanding), with fewer designed for higher-order thinking. Some tools enable self-monitoring and reflection, but few support strategic learning. In the affective domain, AI enhance motivation via personalization but lacks emotional adaptation. These results highlight the need for theory-informed AI design to promote deeper, self-regulated, and emotionally responsive learning.

Generative AI tools such as ChatGPT are rapidly being adopted in Computer Science Education (CSE), offering novel ways to support student learning, particularly in programming, computational thinking, and foundational mathematics. Despite growing interest in these tools, empirical evidence examining their educational impact remains limited. This rapid review synthesizes current research to explore (1) how generative AI tools are being used in CSE classrooms, (2) how students, educators, and professionals perceive their use, and (3) what challenges and opportunities exist for their future integration. Using PRISMA guidelines adapted for rapid review methodology, we conducted a comprehensive search in June 2024 through EBSCO databases, focusing on peer-reviewed empirical studies published since 2022. Of the 64 identified studies, only three met the inclusion criteria, reflecting the emerging nature of this research area. The selected studies show that ChatGPT is primarily used to provide immediate feedback during coding exercises, support debugging processes, and facilitate iterative learning. Students report positive experiences, citing AI’s usefulness in clarifying complex tasks, improving efficiency, and offering 24/7 assistance. However, educators and professionals raise concerns about potential over-reliance on AI, diminished critical thinking, and the erosion of academic integrity. These concerns show the importance of developing pedagogical strategies that balance AI support with human cognitive engagement. Frameworks such as CHAT-ACTS encourage students to self-regulate their interactions with AI tools, while instructional designs like Harvard’s CS50 Duck demonstrate how AI can be integrated to support learning without providing direct answers. Although this review is based on a limited sample, it provides early insights into the affordances and risks of generative AI in CSE. It calls for future research on instructional design, ethical policy development, and longitudinal studies that examine how AI tools shape learning behaviours, skill acquisition, and disciplinary practices over time. Responsible integration of AI in CSE will require thoughtful alignment with educational goals that prioritize higher-order thinking and learner agency.

Many research studies imply that argument mapping can enhance writers' abilities in argumentation and critical thinking; however, we need to consolidate the available evidence as the first step to better understand the scope and coverage of these tools. Therefore, this paper adopts a scoping review methodology guided by the PRISMA-ScR framework. Through this systematic scoping approach, we aim to map the breadth of research on argument visualization and learning by identifying key themes and fields and highlighting gaps in empirical studies. By synthesizing existing evidence across disciplines and contexts, we hope that the scoping review will provide a foundation for future research directions, particularly in the areas of experimental validation and pedagogical design. This consolidation is especially timely given the rapid expansion of online and hybrid learning environments where such tools could provide crucial cognitive scaffolding.

A chatbot is artificial intelligence software that converses with a user in natural language. It can be instrumental in mitigating teaching workloads by coaching or answering student inquiries. To understand student-chatbot interactions, this study is engineered to optimize student learning experience and instructional design. In this study, we developed a chatbot that supplemented disciplinary writing instructions to enhance peer reviewer’s feedback on draft essays. With 23 participants from a lower-division post-secondary education course, we delved into characteristics of student-chatbot interactions. Our analysis revealed students were often overconfident about their learning and comprehension. Drawing on these findings, we propose a new methodology to identify where improvements can be made in conversation patterns in educational chatbots. These guidelines include analyzing interaction pattern logs to progressively redesign chatbot scripts that improve discussions and optimize learning. We describe new methodology providing valuable insights for designing more effective instructional chatbots by enhancing and engaging student learning experiences through improved peer feedback.

This exploratory research conducted a thematic analysis of students’ experiences and utilization of AI tools by students in educational settings. We surveyed 87 undergraduates from two different educational courses at a comprehensive university in Western Canada. Nine integral themes that represent AI’s role in student learning and key issues with respect to AI have been identified. The study yielded three critical insights: the potential of AI to expand educational access for a diverse student body, the necessity for robust ethical frameworks to govern AI, and the benefits of personalized AI-driven support. Based on the results, a model is proposed along with recommendations for an optimal learning environment, where AI facilitates meaningful learning. We argue that integrating AI tools into learning has the potential to promote inclusivity and accessibility by making learning more accessible to diverse students. We also advocate for a shift in perception among educational stakeholders towards AI, calling for de-stigmatization of its use in education. Overall, our findings suggest that academic institutions should establish clear, empirical guidelines defining student conduct with respect to what is considered appropriate AI use.

This chapter begins by questioning the existing practices of writing centre tutoring. Based on the first author's writing centre tutoring experience and some artifacts, such as consultation notes, consultation forms, and feedback on student essays, the authors question whether the writing centre is truly a safe and neutral space for post-secondary writers and whether writing tutoring feedback contains some Eurocentric racial discourses that are complicit and coded in a way that sounds so called objective. Drawing on Lemke's principle of intertextuality, the authors highlight how standardized academic writing expectations have been unconsciously normalized and naturalized in writing centre tutoring discussions, thereby reinforcing the tutor's authority. In the end, we are in the position to look for an alternative, transformative change in the writing centre tutoring practice and a structural shift that can go beyond “remedial writing service provider.”

Artificial Intelligence (AI)’s rise in education brings benefits and challenges. Tools like Grammarly and ChatGPT have been part of our lives, particularly in the education sector. Consequently, it is important to explore how inclusive and adaptable these tools are for students from diverse cultures and languages. This study analyzes survey data from 87 undergraduate students in Canada to identify key themes such as the need for better language translation capabilities, cultural and linguistic inclusivity, and reducing bias and Eurocentrism in AI technologies. These findings form the basis of the Adaptive and Inclusive AI Learning (AIAL) theory, which emphasizes the importance of adaptability, inclusivity, and responsiveness in AI educational technologies to meet the diverse needs for students. The research highlights the need for AI educational tools to advance technologically while remaining culturally sensitive and accessible, in order to promote educational equity and for diverse learners. It suggests a collaborative approach involving cross-cultural developers, educators, and policymakers in creating AI technologies that not only enhance educational outcomes but also respect the diversity of the global student community.

This work-in-progress study aims to explore and analyze the growing impact of large language models (LLMs) in the fields of education and industry. We preliminarily review how LLMs can be integrated into educational contexts with their technical features, open-source nature, and applicability. Through a systematic search, we have identified a selection of open-source LLMs that have been released or significantly updated post-2021. This initial search indicates a thriving field with immense potential for both academic and industry applications. While LLMs hold promise for education, some challenges need to be addressed. These include limited application of open-source LLMs, concerns regarding data privacy, content accuracy, and potential biases. It is critical to carefully consider these factors before deploying LLMs in educational settings. However, our preliminary research highlights the versatility of LLMs in generating educational content and supporting diverse instructional strategies. This suggests a shift towards more adaptive and personalized learning environments. By assessing the suitability of these models for educational purposes, our study lays the foundation for future research aimed at fully maximizing the potential of open-source LLMs to transform teaching and learning practices. As our work progresses, we plan to expand our investigation to explore the broader implications of LLMs on educational outcomes and pedagogical contexts. Ultimately, our goal is to facilitate dynamic, inclusive, and effective learning experiences across various educational environments.

The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.

The CHAT-ACTS pedagogical framework presented in this paper integrates personalized chatbots into active and self-regulated learning (SRL) to enhance student engagement, motivation, and learning outcomes. Employing three primary learning modes - Personalized Chatbot, Self-Regulated Learning, and Active Learning - the learner occupies the central position, symbolizing their active role in shaping their learning journey. Strategic actions such as Evaluation, Feedback, and Plan are crucial in the Personalized Chatbot mode, while the SRL mode emphasizes Goal Setting and Study Tactics. The Active Learning mode underscores Active-Based Learning and Teaching Strategies. Through these modes, bidirectional relationships are established, facilitating feedback, setting goals, and employing active learning techniques. By utilizing this framework, educators can maximize the impact of personalized chatbots in various educational settings.

Developing knowledge-transforming skills in writing may help students increase learning by actively building knowledge, regardless of the domain. However, many undergraduate students struggle to transform knowledge when drafting essays based on multiple sources. Writing analytics can be used to scaffold knowledge transforming as writers bring evidence to bear in supporting claims. We investigated how to automatically identify sentences representing knowledge transformation in argumentative essays. A synthesis of cognitive theories of writing and Bloom's typology identified 22 linguistic features to model processes of knowledge transforming in a corpus of 38 undergraduates' essays. Findings indicate undergraduates mostly paraphrase or copy information from multiple sources rather than engage deeply with sources' content. Eight linguistic features were important for discriminating evidential sentences as telling versus transforming source knowledge. We trained a machine learning algorithm that accurately classified nearly three of four evidential sentences as knowledge-telling or knowledge-transforming, offering potential for use in future research.

In the present study, we developed a chatbot that helps teachers to deliver writing instructions. By working with the chatbot, the post-secondary writers developed a thesis statement for their argumentative essay outlines, and the chatbot helped the writers to refine their peer review feedback. We conducted a preliminary analysis of the effect of a chatbot on these writers’ writing achievement. We also collected several student testimonials about their chatbot experiences. Several important pedagogical and research implications for chatbot-guided writing instructions and the use of learning technology have been addressed.

Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gašević, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne’s (1982) characterization of traces — ambient data gathered as learners study that more clearly represent which operations learners apply to which information — and his COPES model of a learning event — conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy’s tools. nStudy can be configured to support learners’ evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

Writing centres offer a safe space for writers, including English-as-additional-language (EAL) students, to negotiate meaning and become more