Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and ...regions, large language models are here to stay. This commentary presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.
•Current Research and Applications of Large Language Models in Education•Opportunities and Challenges Related to the Use of Language Models in Education from Teacher and Student Perspective•Opportunities of Using Large Language Models for Innovative Educational Technologies•Key Challenges, Risks, and Mitigation Strategies Related to the Application of Large Language Models in Education•Further Issues and Discussion Related to User Interfaces, Fair Access, Ethical and Responsible Use for such Technologies
Abstract This paper investigates the strategic behavior of large language models (LLMs) across various game-theoretic settings, scrutinizing the interplay between game structure and contextual ...framing in decision-making. We focus our analysis on three advanced LLMs—GPT-3.5, GPT-4, and LLaMa-2—and how they navigate both the intrinsic aspects of different games and the nuances of their surrounding contexts. Our results highlight discernible patterns in each model’s strategic approach. GPT-3.5 shows significant sensitivity to context but lags in its capacity for abstract strategic decision making. Conversely, both GPT-4 and LLaMa-2 demonstrate a more balanced sensitivity to game structures and contexts, albeit with crucial differences. Specifically, GPT-4 prioritizes the internal mechanics of the game over its contextual backdrop but does so with only a coarse differentiation among game types. In contrast, LLaMa-2 reflects a more granular understanding of individual game structures, while also giving due weight to contextual elements. This suggests that LLaMa-2 is better equipped to navigate the subtleties of different strategic scenarios while also incorporating context into its decision-making, whereas GPT-4 adopts a more generalized, structure-centric strategy.
The emergence of OpenAI's ChatGPT has put intense spotlight on Generative AI (Gen-AI) systems and their possible impacts on Academic integrity. This paper provides an overview of the current ...arguments around ChatGPT and Academic integrity and concludes that although these technologies are capable of revolutionising academia, the way ChatGPT and other generative AI systems are used could surely undermine academic integrity. However, to ensure that the risks to academic integrity are mitigated for greater maximisation, institutional and multi-stakeholder efforts are required.
We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human ...researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities.
The power of using artificial intelligence (AI)‐human collaboration to advance reticular chemistry is shown. Through iterative learning, the GPT‐4 Reticular Chemist indicates instructions while evolving based on the feedback from a human researcher. It results in the innovative discovery of a series of isoreticular metal–organic frameworks (MOFs), and suggests an approach for using large language models in experimental scientific research.
Der KI‐basierte Chatbot „ChatGTP“ zog in kürzester Zeit viel Aufmerksamkeit auf sich, da er einen leichten und unmittelbaren Zugang sowie die Nutzung einer künstlichen Intelligenz (KI) bietet. Der ...Chatbot löst dabei unterschiedlichste Aufgaben und gibt Antworten auf die ihm gestellten Fragen. Hierzu können dem Chatbot sehr komplexe Aufgabenstellungen auch aus dem Chemieunterricht präsentiert werden, die dieser umfangreich und (zum Teil) korrekt bearbeitet. Diese neuen Möglichkeiten stellt das Lehren und Lernen vor eine neue Situation, in der eine KI die (Haus‐)Aufgaben für Schüler:innen lösen kann. Wie weit diese Technologie auf der Basis von Large Language Models ist und welche neuen Herausforderungen dies für das Lehren und Lernen im Chemieunterricht mit sich bringen kann, wird anhand von Beispielaufgaben zu Tensiden diskutiert.
There are many myths regarding Alzheimer's disease (AD) that have been circulated on the internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models, ...such as ChatGPT, may be a valuable tool to help assess these myths for veracity and inaccuracy; however, they can induce misinformation as well. This study assesses ChatGPT's ability to identify and address AD myths with reliable information. We conducted a cross-sectional study of attending geriatric medicine clinicians' evaluation of ChatGPT (GPT 4.0) responses to 16 selected AD myths. We prompted ChatGPT to express its opinion on each myth and implemented a survey using REDCap to determine the degree to which clinicians agreed with the accuracy of each of ChatGPT's explanations. We also collected their explanations of any disagreements with ChatGPT's responses. We used a 5-category Likert-type scale with a score ranging from −2 to 2 to quantify clinicians' agreement in each aspect of the evaluation. The clinicians (n = 10) were generally satisfied with ChatGPT's explanations. Among the 16 myths, the clinicians were generally satisfied with these explanations, with (mean SD score of 1.1±0.3). Most clinicians selected “Agree” or “Strongly Agree” for each statement. Some statements obtained a small number of “Disagree” responses. There were no “Strongly Disagree” responses. Most surveyed health care professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation; however, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.
The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the ...truth, automated assistance is urgently needed. Current state-of-the-art approaches often involve fine-tuning existing models with contributions from domain knowledge bases. The black-box nature and interpretability issues of deep neural networks (DNNs) have increased interest in hybrid approaches, giving rise to Deep Neural Fuzzy Systems (DNFSs). This research paper presents the Hybrid Fact-Checking Framework (HFCF) leveraging a DNFS tailored to address the uncertainty inherent in fact verification tasks and enhance the reliability of model responses. The DNFS integrates a Large Language Model (LLM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS) for automated fact verification. The framework utilizes relevant evidence from open-world and closed-world sources, leveraging deep language models and employing few-shot prompting without additional training to generate and justify verdicts. Including fuzzy rules and considering the trustworthiness and relevance of retrieved evidence enhances response reliability, thereby improving overall effectiveness and outcome interpretability. Experimental validations have been conducted on three publicly available datasets ranging in different domains of expertise: ClimateFEVER, SciFact, and FEVER. The results demonstrate that the proposed framework ensures better outcomes, transparency, and mindful decision-making.
