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New papers on fairness, safety, alignment and governance.
Digital innovation as a bank risk mitigator: Empirical insights from Chinese commercial banks
Publication date: September 2026 Source: Research Policy, Volume 55, Issue 7 Author(s): Shengjing Yu, Xiaolan Zheng, Martin J. Liu
Play with AI (PL-AI): A play-centered, design-based curriculum for AI literacy in pre-K and kindergarten
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Joohi Lee
Social networks and technology adoption: Evidence from online banking
Publication date: September 2026 Source: Research Policy, Volume 55, Issue 7 Author(s): Qi Chen, Roberto M. Samaniego
A framework for evaluation of large language models in essay assessment: Reliability, alignment, and causal reasoning
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Tongxi Liu, Luyao Ye, Wei Yan
The coevolution of financial and technological innovations
Publication date: September 2026 Source: Research Policy, Volume 55, Issue 7 Author(s): Lin William Cong, Po-Hsuan Hsu, John P. Walsh
Pedagogy first, technology second: Cross-level relationships between teacher professional knowledge and student learning in artificial intelligence (AI) education
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Weipeng Shen, Ching-sing Chai, Thomas K.F. Chiu, King Woon Yau, Helen Meng, Irwin King, Savio Wong, Yeung Yam
Editorial Board
Publication date: September 2026 Source: Research Policy, Volume 55, Issue 7 Author(s):
Value-sensitive design in action: Designing student-centered intelligent tutoring systems with community college students and instructors
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Eunhye Grace Ko, Joan E. Hughes
Corrigendum to ‘Making sense of wickedness: Directionality heuristics for challenge-led innovation policy’ [Research Policy 55 (2026) Article 105513]
Publication date: Available online 2 June 2026 Source: Research Policy Author(s): Matthias Mueller, Michael P. Schlaile, Stephanie Lang, Matthijs J. Janssen, Iris Wanzenböck, Kristina Bogner, Jana Zscheischler, Michael Schramm, Andreas Pyka
Directive, metacognitive, or a blend of both? A comparison of AI-generated feedback types on student engagement, confidence, and outcomes
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge, Omid Noroozi, Dragan Gašević, Marie Boden, Hassan Khosravi
From pitch to progress: The interplay of team reputation and governance in crowdfunded innovation
Publication date: Available online 29 May 2026 Source: Research Policy Author(s): Xin Deng, Yen Teik Lee, Qi Sun, Yu Yan
Integrating artificial intelligence and data envelopment analysis for sustainable efficiency assessment in higher education
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): William Villegas-Ch, Rommel Gutierrez, Angel Jaramillo-Alcazar, Alexandra Maldonado-Navarro
Optimizing automated scoring in ILSAs with prompt compression
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Ji Yoon Jung, Ummugul Bezirhan, Matthias von Davier
Unleashing human potential: An artificial intelligence competency framework for K–12 education
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Siu Cheung Kong, Wenxi Hu
What undergraduate students need to know and actually know about generative AI
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Sina Rismanchian, Eesha Tur Razia Babar, Shayan Doroudi
Exploring the relationship between empowerment in using artificial intelligence for problem-solving and artificial intelligence ethical awareness: Multi-group structural equation modelling
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Siu Cheung Kong, Jinyu Zhu
Conversational AI in children's home literacy learning: effectiveness, advantages, challenges, and family perception
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Shuang Quan, Xintian Tu-Shea, Yi Ding, Yao Du, Qingxiao Zheng, Laney E. Gerdich
Artificial intelligence literacy at school: A systematic review with a focus on psychological foundations
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Shuyan Feng, Astrid Carolus
Enhancing AI literacy for educators: Where to start and to what end?
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Bo Pei, Jie Lu, Zhaowei Zhang, Priscilla Tuffour, Sanghoon Park
Generative AI in higher education: A bibliometric review of emerging trends, power dynamics, and global research landscapes
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Kun Dai, Yabing Liu, Xiaofan Zhang
Large language models for education: An open-source paradigm for automated Q&A in the graduate classroom
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Ryann M. Perez, Marie Shimogawa, Yanan Chang, Xinning Li, Hoang Anh T. Phan, Jason G. Marmorstein, Evan S.K. Yanagawa, E. James Petersson
Less stress, better scores, same learning: The dissociation of performance and learning in AI-supported programming education
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Patrick Bassner, Ben Lenk-Ostendorf, Ramona Beinstingel, Tobias Wasner, Stephan Krusche
LLM sentiment quantification reveals selective alignment with human course-evaluation raters
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Joyce W. Lacy, Chi Nnoka, Zachary Jock, Cathleen Morreale
A LLM-based pedagogical framework for active, inquiry-based and adaptive learning in L2 writing
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Ruonan Wang, Yan Yin, Yongbo Cao
Modeling generative AI adoption in higher education: An integrated TAM–TPB–SDT framework with SEM validation
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Dina Tbaishat, Omar AlFandi, Faten Hamad, Syed Muhammad Salman Bukhari, Suha Al Muhaissen
Empowering university teachers in higher education: A generative AI-responsive competency framework
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Daner Sun, Shen Ba, Yingying Cha, Jiahui Yu, Feng-Kuang Chiang, Hai Min Dai, Cher-Ping Lim
EvalYaks : Instruction tuning datasets and LoRA fine-tuned models for automated scoring of CEFR B2 speaking assessment transcripts
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Nicy Scaria, Silvester John Joseph Kennedy, Thomas Latinovich, Deepak Subramani
Can students judge like experts? A large-scale study on the pedagogical quality of AI and human personalized formative feedback
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Tanya Nazaretsky, Hagit Gabbay, Tanja Käser
From knowledge gaps to learning opportunities: Leveraging student questions and dual use of generative AI to support student learning at scale
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Stanislav Pozdniakov, Jonathan Brazil, Oleksandra Poquet, Stephan Krusche, Santiago Berrezueta-Guzman, Shazia Sadiq, Hassan Khosravi
How reliable are large language models in analyzing the quality of written lesson plans? A mixed-methods study from a teacher internship program
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Dennis Hauk, Nina Soujon
From teachers to chatbots: Scaffolded corrective feedback and student trust in online L2 English classrooms
Publication date: June 2026 Source: Computers and Education: Artificial Intelligence, Volume 10 Author(s): Ali Soyoof, Barry Lee Reynolds, Ehsan Rassaei, Chian-Wen Kao, Xuan Van Ha
Concentration and Specialty Pair Patterns of Interdepartmental Consultations in Hospitalized Patients Using Real-World Data: Retrospective Cohort Study
Background: Interdepartmental consultations are essential for managing complex inpatient care but are often inefficient. Hospital-wide, data-driven analyses are needed to guide process improvements; yet, most existing studies have focused on single departments or specific diseases, leaving a gap in understanding hospital-level collaboration networks. Understanding these patterns is crucial for optimizing clinical workflows, reducing delays, and improving patient outcomes in large tertiary hospit
Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis
Background: Large language models (LLMs) show growing potential for decision support. However, integrating domain-specific medical knowledge while maintaining accuracy, safety, and interpretability remains challenging for postoperative discharge instructions and patient education. Fine-tuning, retrieval-augmented generation (RAG), and hybrid fine-tuning+RAG approaches are prominent strategies for knowledge integration, but their comparative performance in postoperative care has not been systemat
Factors Shaping Trust and Satisfaction With AI Medical Chatbots: A Mixed Methods Vignette Survey of Caregivers Seeking Guidance on Pediatric Infectious Diseases
Background: As artificial intelligence (AI) chatbots become an increasingly common source of quick medical guidance, it is important to understand whether their responses meet users’ needs and support well-informed health decisions. Yet, existing evaluation frameworks rely primarily on expert-defined evaluation dimensions that have not been empirically validated with end users. It remains unclear whether these frameworks capture the criteria people actually use when judging a response to be usef
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used
Real-time fall detection based on vision for low-power edge platforms
Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate such memory almost exclusively through downstream question answering, scoring only the correctness of a final answer. This black-box formulation conflates the heterogeneous causes of memory failure, such as missing the introduction of a relevant fact, binding an operation to the wrong target, or relying on stale values
UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical. Since such dense labels are rarely available at scale, normalized time within a demonstration is often used as a scalable substitute: later frames are treated as higher progress. However, this time-derived label is only a noisy proxy for physical task progress. In contact-rich manipulat
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
Clinical notes contain many of the signs and symptoms that bring patients to care, yet this information rarely reaches structured fields. Existing extraction approaches either rely on context-insensitive rules that generate false positives or on supervised models that require substantial fine-tuning. We present Pythia, a multi-agent system that autonomously writes and optimizes extraction prompts for clinical concepts without manual prompt engineering or fine-tuning. Running on a locally hosted
Toward Localizing and Repairing Bias in Transformer Attention Heads
Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and repair methods largely operate at the input-output or retraining level, while recent work suggests that bias-related behavior can concentrate in a small set of attention heads. This paper studies whether attention heads can be localized and repaired through a targeted inference-time intervention. We introduce ROBIN, a