As Generative Artificial Intelligence (GenAI) tools collect data from both reliable and unreliable sources across the internet, concerns have been raised about the credibility of the output content learners are exposed to when seeking these tools for assistance. To address this problematic issue, our study tests the development of a pedagogical assistant trained on a personalized course framework; Besides its equipment with content that aligns with classroom lectures, the virtual assistant was given a comprehensive set of instructions guiding its behavior; Restricting its responses to the provided course material, forbidding the provision of information from any external sources as an initial action to battle against information credibility issues, and limiting its interactions to course-related discussions to promote engagement and mitigate distractions. The study explores the perceived credibility of the tested agent alongside the perceived impact on students’ learning engagement. This study is significant in informing the design of credible, curriculum-aligned AI assistants for EFL learning contexts. To achieve the required results, our study adopts DeLone & McLean’s theoretical framework alongside a quantitative research design with a structured questionnaire as a data-gathering tool. The sample of this study consists of N = 63 students of the Higher School of Teachers, Moulay Ismail University. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Students exhibited positive perceptions towards the custom agent, which they perceived as an engaging and credible source of information that also aligns with the course content they are exposed to during formal lectures. Our findings also revealed a strong correlation between Perceived Impact on Learning Engagement (PILE) and Perceived Credibility (PC), with r (61) = .780. The study acknowledges some limitations and offers recommendations for future studies.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.