AI in Education Research — research article summaries and concept syntheseshttps://edtechdev.github.io/en-ushttps://edtechdev.github.io/aied/articles/acceptance-ai-english-tools-2026/https://edtechdev.github.io/aied/articles/acceptance-ai-english-tools-2026/> **Synthesis:** Wu et al. (2026) examined how learning motivation, self-efficacy, anxiety, and risk perception relate to acceptance of AI-assisted English language learning in a Chinese higher-education context, building on the Technology Acceptance Model (TAM). Drawing on survey data from 210 undergraduates (STEM = 91, Humanities = 119; English proficiency Low = 77, Intermediate = 103, High = 30), they found that learning motivation and self-efficacy were consistently and positively associatedThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-autonomous-learning-accomplishment-2026/https://edtechdev.github.io/aied/articles/ai-autonomous-learning-accomplishment-2026/> **Synthesis:** Wang and Zhang (2026) examined how AI-assisted autonomous learning relates to reduced academic accomplishment among 1,264 vocational college students in China, focusing on the mediating role of hardiness (commitment, control, challenge). Using structural equation modeling, they found AI-assisted autonomous learning was negatively associated with hardiness and positively associated with reduced academic accomplishment, with hardiness partially mediating the relationship — the indThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-feedback-enactment-workflow-2026/https://edtechdev.github.io/aied/articles/ai-feedback-enactment-workflow-2026/> **Synthesis:** Alsaiari et al. (2026) report a large-scale quasi-experimental cohort study (13,037 students; 51,296 student-authored resources) comparing three AI-mediated feedback workflows. Students in the **Enacted Feedback** condition — prompted to select feedback suggestions, evaluate their relevance, and engage in targeted AI dialogue anchored to those selections — showed significantly higher uptake of AI-generated feedback (26.2% estimated probability) than **Directed Feedback** (14.1%)Thu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-generated-smartphone-circular-motion-lab-2026/https://edtechdev.github.io/aied/articles/ai-generated-smartphone-circular-motion-lab-2026/> **Synthesis:** Suñer et al. (2026) show that a fully customized, browser-based rotation laboratory can be generated entirely through natural-language prompting of an AI assistant, with no manual coding. Most smartphone physics experiments rely on precompiled sensor apps whose interfaces cannot be tailored to a specific activity, and customized labs previously required programming knowledge beyond most teachers. Using the AI-generated lab with a simple rotating platform, they characterize unifoThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-literacy-heptagon-2026/https://edtechdev.github.io/aied/articles/ai-literacy-heptagon-2026/> **Synthesis:** Hackl, Müller, and Sailer (2026) present the AI Literacy Heptagon, a structured seven-dimensional framework for AI literacy (AIL) in higher education, developed through an integrative literature review of publications from 2021–2024. The framework synthesizes seven core dimensions — technical knowledge and skills, application proficiency, critical thinking ability, ethical awareness and reasoning, social impact understanding, integration skills, and legal and regulatory knowledgThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-tutoring-quality-k12-methodologies-2026/https://edtechdev.github.io/aied/articles/ai-tutoring-quality-k12-methodologies-2026/> **Synthesis:** Udeshi et al. (2026), the team behind **Khanmigo** (Khan Academy's K-12 AI tutor, launched 2023), describe the metrics they use to measure AI tutoring quality and student engagement, along with the live experiments that have moved those metrics. Given that LLMs are opaque black boxes, they argue robust evaluation and live experimentation are essential. The paper highlights changes across models, prompting, personalization, and agents that improved tutoring outcomes. Accepted at Thu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-use-critical-thinking-medical-students-2026/https://edtechdev.github.io/aied/articles/ai-use-critical-thinking-medical-students-2026/> **Synthesis:** Arshad et al. (2026) examined how AI-based educational technology influences critical thinking among 480 undergraduate medical students in Pakistan, using a cross-sectional design and Hayes' PROCESS Model 14. They found that AI use was positively associated with critical thinking and self-regulated learning, while cognitive load negatively related to both. Cognitive load partially mediated the AI-use→critical-thinking link, and self-regulated learning significantly moderated thaThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-vocal-pedagogy-2026/https://edtechdev.github.io/aied/articles/ai-vocal-pedagogy-2026/> **Synthesis:** Li (2026) presents a conceptual Perspective arguing that AI-assisted vocal pedagogy should be evaluated not by how precisely AI measures vocal output (pitch, stability, timing) but by how AI-generated evidence becomes meaningful for human learning — how learners interpret feedback, regulate practice, sustain motivation, and develop trust in teacher-guided processes. The article proposes a three-level framework linking technical adaptation, human learning processes, and educationThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/cyberagents-gamified-cybersecurity-learning-2026/https://edtechdev.github.io/aied/articles/cyberagents-gamified-cybersecurity-learning-2026/> **Synthesis:** Hornung et al. (2026) present **CyberAGENTS**, an agentic framework for gamified cybersecurity learning that enables *structured autonomy* through ontology-guided validation, schema-governed behavioral control, and competency-based progression. The learning loop is decomposed into four specialized agents (challenge, support, evaluation, reward), each governed by behavioral schemas, with a cybersecurity ontology validating all generated content before display. Classroom deploymenThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/elbench-education-llm-benchmark-2026/https://edtechdev.github.io/aied/articles/elbench-education-llm-benchmark-2026/> **Synthesis:** Jiang et al. (2026) introduce **ELBench**, the first benchmark to evaluate education-facing LLMs on all four required dimensions — General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation — under a common protocol, combining curated public sources with newly synthesized safety and cultivation data. Testing nine models, they find module-level profiles are more informative than a single aggregate: the top six models are statistically indistinguisThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/embodied-inquiry-ai-facilitator-physics-2026/https://edtechdev.github.io/aied/articles/embodied-inquiry-ai-facilitator-physics-2026/> **Synthesis:** Tufino & Damiani (2026) explore where a language-based AI can stand within an inquiry activity without displacing embodied experience, using a Master's-level physics education course investigating the statics of fluids via the ISLE approach. In a two-phase design, students first built the buoyancy model with their own hands without AI; a purpose-configured AI assistant then facilitated applying the model to a new phenomenon. The paper discusses what a language-based facilitator Thu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-ar-physics-simulation-prompt-2026/https://edtechdev.github.io/aied/articles/genai-ar-physics-simulation-prompt-2026/> **Synthesis:** Levy et al. (2026) show how a structured natural-language prompt can generate a browser-based, hand-controlled **augmented-reality (AR) physics simulation** — spread your thumb and index finger and a virtual lamp changes color — and describe its use in an introductory physics class. Computer simulations have a long record of supporting physics learning by making abstract concepts interactive, and generative AI now lowers the barrier to creating customized, embodied, interactive Thu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-differentiated-eap-reading-materials-2026/https://edtechdev.github.io/aied/articles/genai-differentiated-eap-reading-materials-2026/> **Synthesis:** Gao (2026) examined whether generative-AI-supported adaptation of English for Academic Purposes (EAP) reading materials chiefly changes passage-level structural complexity or text-embedded functional support. Using a role-prompted workflow (barrier analysis, adaptation, fidelity checking, validation) and a 3×3 between-subjects design (N=135; proficiency × material condition), the study found that GenAI-supported differentiation operates primarily at the level of proficiency-specThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-motivation-engagement-2026/https://edtechdev.github.io/aied/articles/genai-motivation-engagement-2026/> **Synthesis:** Ahmed and Sultan (2026) investigated how perceived autonomy, competence, relatedness, expectancy, and value influence autonomy support for AI use, autonomous motivation, and ultimately student motivation and engagement in GenAI-supported learning. Integrating Self-Determination Theory, Expectancy-Value Theory, and the Technology Acceptance Model, and analyzing data from 297 undergraduates and postgraduates at King Saud University (Saudi Arabia) with PLS-SEM, they found that autoThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-over-reliance-learning-2026/https://edtechdev.github.io/aied/articles/genai-over-reliance-learning-2026/> **Synthesis:** Gao, Sun, and Khan (2026) developed a dual-pathway model examining both the positive and negative effects of generative AI use on sustainable learning performance, integrating AI literacy, self-regulated learning, cognitive offloading, and individual differences (polychronicity). Using a mixed-method design with three-wave time-lagged survey data from 623 Chinese university students plus educator interviews, they found that AI literacy significantly enhances critical AI evaluatiThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-pronunciation-feedback-wtc-2026/https://edtechdev.github.io/aied/articles/genai-pronunciation-feedback-wtc-2026/> **Synthesis:** Lu et al. (2026) examined, through the lens of Social Cognitive Theory, whether Chinese university EFL learners' perceptions of generative-AI-based pronunciation feedback relate to their willingness to communicate (WTC) in English, with English pronunciation self-efficacy as a hypothesized mediator. Using a cross-sectional survey of 1,701 learners, covariance-based structural equation modeling, and bias-corrected bootstrapping, they found that positive perceptions of GenAI pronuThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/hdr-brachytherapy-agentic-ai-simulation-2026/https://edtechdev.github.io/aied/articles/hdr-brachytherapy-agentic-ai-simulation-2026/> **Synthesis:** Xu et al. (2026) present an agentic AI-driven immersive simulation for training in **High Dose Rate (HDR) brachytherapy**, integrating VR and mobile computing to create a high-fidelity, risk-free environment for mastering complex procedural skills. A knowledge-aware assistant uses [[rag|Retrieval-Augmented Generation]] to ground agent interactions in authoritative clinical guidelines, providing natural-language interfaces and hands-free, real-time guidance during intricate maneuThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/inside-llm-student-simulator-reasoning-2026/https://edtechdev.github.io/aied/articles/inside-llm-student-simulator-reasoning-2026/> **Synthesis:** Niousha, Kang, & Norouzi (2026) introduce **INTERNAL STUDENT DIALOGUE (INSIDE)**, a student modeling framework that fine-tunes LLMs to both *act* like students and *think* like them. Two students may submit identical work for entirely different reasons, so INSIDE generates internal dialogue grounded in Bloom's Taxonomy across cognitive, affective, and action dimensions, fine-tuning on paired think-traces and actions. Evaluated against prompting baselines, INSIDE improves action Thu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/physics-chatbot-epistemological-beliefs-2026/https://edtechdev.github.io/aied/articles/physics-chatbot-epistemological-beliefs-2026/> **Synthesis:** Sirnoorkar & Mamidpalliwar (2026) investigate the association between introductory physics students' preferences for chatbot behavior and their epistemological beliefs, using a custom online waves module with simulations integrated with a chatbot. Preferences were captured through three options (guided-inquiry, direct answer, and a combination); beliefs via the EBAPS survey. Students who preferred chatbots that initially engage in guided-inquiry but provide answers when explicitThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/residencyrl-clinical-rl-training-2026/https://edtechdev.github.io/aied/articles/residencyrl-clinical-rl-training-2026/> **Synthesis:** Liévin et al. (2026) present **ResidencyRL**, a reinforcement learning method for training clinical AI agents through simulated multi-turn clinical encounters (up to 60 dialogue turns and 8 tool calls per trajectory). It pairs the policy agent with LLM simulators capable of complex, adversarial behaviors, training against a structured reward aligned to diagnostic accuracy, management quality, communication, documentation, and safety. On held-out evaluation the agent improves diaThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/structural-silence-underrepresented-language-ai-2026/https://edtechdev.github.io/aied/articles/structural-silence-underrepresented-language-ai-2026/> **Synthesis:** Roy & Roy (2026) argue that the **infrastructure of AI** — training corpora, tokenization, benchmarks, deployment architectures — systematically disadvantages speakers of underrepresented languages *before a model is trained*, reframing dataset scarcity as a structural barrier rather than an isolated technical limitation. Using Bengali as a case in AI-assisted education, they document four interlocking failures: a web-presence gap (<0.5% of global content for ~4% of the populatiThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/t2i-competence-paradox-2026/https://edtechdev.github.io/aied/articles/t2i-competence-paradox-2026/> **Synthesis:** Liu, Meng, and Zhang (2026) examined technology acceptance of text-to-image (T2I) generative AI in art and design education from both educators' and students' perspectives, using a modified exploratory sequential mixed-methods design (QUAL-QUAN-qual). Based on instructor focus groups, a survey of 417 college students, and semi-structured interviews, they found that performance expectancy, social influence, novelty value, and creative competence positively influence behavioral inThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/teaching-monster-pck-benchmark-2026/https://edtechdev.github.io/aied/articles/teaching-monster-pck-benchmark-2026/> **Synthesis:** Lin et al. (2026) present the **Teaching Monster Challenge**, the first instructional-video generation benchmark that treats the learner persona as an explicit evaluation criterion, measuring whether AI agents can adapt a lesson to a specified learner — [[teacher-ai-competency|Pedagogical Content Knowledge (PCK)]]. Systems receive a topic and a learner persona and must generate a complete instructional video, screened by an LLM-judge, ranked by crowd pairwise voting, and finalizThu, 13 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/agentschool-multi-agent-simulation-education-2026/https://edtechdev.github.io/aied/articles/agentschool-multi-agent-simulation-education-2026/> Ye et al. (2026) introduce **AgentSchool**, an LLM-driven multi-agent [[simulating-students|simulator]] that models learning as **state transition rather than prompted behavior**. It couples cognitively growable student agents (weighted subject knowledge graphs, thinking-workflow pools, explicit misconceptions) with adaptive teacher agents that plan, scaffold, and reflect along the [[zone-of-proximal-development]], embedded in a configurable scenery generator and a multi-scale simulator. It prWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-acceptance-preservice-science-teachers-2026/https://edtechdev.github.io/aied/articles/ai-acceptance-preservice-science-teachers-2026/> **Synthesis:** This survey of 380 pre-service science teachers in Ghana, guided by UTAUT and the Theory of Planned Behaviour, finds generally positive perceptions of AI and strong intentions to use it, with ChatGPT the most frequently used tool for research, content explanation, and lesson planning. Positive attitudes and favorable effort expectancy were not fully matched by actual adoption, indicating an acceptance-to-use gap.Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-generated-interactive-fiction-education-2026/https://edtechdev.github.io/aied/articles/ai-generated-interactive-fiction-education-2026/> **Synthesis:** This pilot study (N = 22 STEM higher-education students) evaluates AI-generated interactive fiction as an educational medium. Narrative clarity and length acceptance rated positively, engagement hovered near neutral, and story-content coherence was the weakest dimension — with quiz integration emerging as the main usability bottleneck. The authors derive concrete design implications for interactive, narrative learning experiences.Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-guided-learning-audiovideo-2026/https://edtechdev.github.io/aied/articles/ai-guided-learning-audiovideo-2026/> **Synthesis:** This dissertation develops an AI-guided learning framework that supports three interconnected stages — Consume, Understand, and Imitate — with three deep-learning systems for audio/video learning. AIxSpeed adapts audio playback speed using speech-recognition confidence; FastPerson produces multimodal video summaries; and Profy supports pronunciation practice from largely unannotated speech. Evaluations show efficiency gains (up to 1.30x playback, 53% less viewing time) with no lWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-interior-design-malaysia-2026/https://edtechdev.github.io/aied/articles/ai-interior-design-malaysia-2026/> **Synthesis:** This article examines how generative AI and intelligent visualization platforms are reshaping interior design practice in Malaysia, shifting designers from primary form-generators toward critical mediators and curators of machine outputs. It explores the implications for university curricula, arguing that professional education must integrate technical proficiency with critical and ethical judgment, and addresses emerging needs for professional regulation.Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/chatgpt-hints-human-tutor-learning-gains-2024/https://edtechdev.github.io/aied/articles/chatgpt-hints-human-tutor-learning-gains-2024/> Pardos & Bhandari (2024) report a randomized efficacy study (N=274) comparing ChatGPT-generated hints to human tutor-authored hints and a no-help control across four mathematics subject areas. Only the ChatGPT condition produced statistically significant learning gains versus control, with no significant difference between ChatGPT and human-authored hints — and ChatGPT's 32% raw hint-error rate was reducible to near zero (algebra) or 13% (statistics) using the self-consistency hallucination-miWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/competency-based-education-genai-production-2026/https://edtechdev.github.io/aied/articles/competency-based-education-genai-production-2026/> **Synthesis:** This conceptual paper proposes adding *production* — the capability to deliver professional-standard work by directing tools and other people — as a fourth attribute of competency-based education (CBE), alongside knowledge, skills, and attitudes/values. The proposal responds to a construct-validity problem: generative AI has severed the inference from a student-produced artifact to the student's own knowledge and skill, and production supplies the missing interpretation for the Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/critical-media-literacy-education-2026/https://edtechdev.github.io/aied/articles/critical-media-literacy-education-2026/> **Synthesis:** Based on expert interviews and a survey of 141 university students in Communication and Education programs, this study finds that while technology offers real opportunities for teaching and learning, its inclusion in the curriculum is limited and often superficial. Teachers are under-trained to manage tools that produce disinformation, deepfakes, and fake news, which hinders students' critical thinking. The authors argue for critical media literacy that lets students question anWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/genai-student-experiences-uk-he-survey-2026/https://edtechdev.github.io/aied/articles/genai-student-experiences-uk-he-survey-2026/> **Synthesis:** The StudentXGenAI Project surveyed more than 7,000 students across 7 UK institutions (September–December 2025) on GenAI use in their studies, comparing findings with a companion Australian survey. A significant minority of students conscientiously object to GenAI use, while most users are honest most of the time and try to avoid submitting direct GenAI outputs — yet students still use GenAI throughout the entire learning and assessment process, creating a persistent tension betwWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/haiml-human-centered-ai-metacognitive-model-2026/https://edtechdev.github.io/aied/articles/haiml-human-centered-ai-metacognitive-model-2026/> **Synthesis:** HAIML is a human-centered framework for learning in AI-supported environments that preserves human agency, metacognitive awareness, ethical reasoning, and personal responsibility. Grounded in self-efficacy, self-regulated learning, experiential learning, metacognition, and automation-bias research, the model spans three interconnected layers — Experiential AI Use, Metacognitive Reflection, and Ethical Decision-Making — guiding learners from direct engagement with AI to reflectivWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/metacognitively-discordant-completion-genai-2026/https://edtechdev.github.io/aied/articles/metacognitively-discordant-completion-genai-2026/> **Synthesis:** This theoretical paper names a state it calls *metacognitively discordant completion* (MDC): a learner submits correct, complete work while holding a first-person awareness that understanding has not actually arrived. Arguing that no existing literature holds the three defining conditions together under one name, the author builds the construct by inheritance from metacognition research and by boundary against related concepts, framing GenAI's role as amplification rather than iWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/multimodal-item-parameter-estimation-2026/https://edtechdev.github.io/aied/articles/multimodal-item-parameter-estimation-2026/> **Synthesis:** This paper fine-tunes a multimodal large language model (Qwen3.5-based) to reconstruct multiple-choice model (MCM) and three-parameter logistic (3PL) item characteristic curves. By learning to reproduce students' systematic error patterns across a range of ability levels, the LLM implicitly captures underlying response probabilities and can approximate item difficulty on held-out test items directly from predicted option probabilities.Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/oatutor-open-source-adaptive-tutor-2023/https://edtechdev.github.io/aied/articles/oatutor-open-source-adaptive-tutor-2023/> OATutor (Open Adaptive Tutor) is the first open-source adaptive tutoring system built on Intelligent Tutoring System (ITS) principles, developed at UC Berkeley's CAHL Lab. It combines an MIT-licensed, fully engineered codebase with a Creative Commons (CC BY) algebra content library, knowledge tracing, A/B testing infrastructure, and LTI support — designed to democratize adaptive learning research by removing the barrier to replicating and extending experiments that proprietary platforms createWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/simulating-students-diverse-cognitive-levels-2025/https://edtechdev.github.io/aied/articles/simulating-students-diverse-cognitive-levels-2025/> Wu et al. (2025, ACL) tackle the core challenge of [[simulating-students]]: LLMs trained as "helpful assistants" produce overly perfect answers and fail to model the natural imperfections and varied cognitive levels of real learners. They propose a training-free framework that builds a cognitive prototype of each student from a knowledge graph, predicts performance on new tasks, and iteratively refines simulated solutions via beam search to reproduce realistic mistakes — achieving a 100% improWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/simulating-students-llm-review-2026/https://edtechdev.github.io/aied/articles/simulating-students-llm-review-2026/> Marquez-Carpintero, Lopez-Sellers & Cazorla (2025) present a thematic review of empirical and methodological studies using LLMs to [[simulating-students|simulate student behavior]] in education. They synthesize evidence on how LLM-based agents emulate learner archetypes, respond to instructional inputs, and interact in multi-agent classroom scenarios, and examine implications for curriculum development, instructional evaluation, and teacher training — while flagging persistent concerns around Wed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/valid-student-simulation-llm-2026/https://edtechdev.github.io/aied/articles/valid-student-simulation-llm-2026/> Yuan et al. (2026) present a conceptual and methodological framework for valid LLM-based [[simulating-students|student simulation]]. They identify the **competence paradox** — broadly capable LLMs asked to emulate partially knowledgeable learners produce unrealistic error patterns and learning dynamics — and reframe student simulation as a constrained generation problem governed by an explicit **Epistemic State Specification (ESS)** that defines what a simulated learner can access, how its errWed, 12 Aug 2026 00:00:00 GMThttps://edtechdev.github.io/aied/articles/ai-assisted-collaborative-learning-model-dbr/https://edtechdev.github.io/aied/articles/ai-assisted-collaborative-learning-model-dbr/> **Synthesis:** Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher EducationTue, 11 Aug 2026 00:00:00 GMT