'We could have asked ChatGPT': students fight back over course taught by AI Students at the University of Staffordshire have said they feel "robbed of knowledge and enjoyment" after a course they hoped would launch their digital careers turned out to be taught in large part by AI. James and Owen were among 41 students who took a coding module at Staffordshire last year, hoping to change careers through a government-funded apprenticeship programme designed to help them become cybersecurity experts or software engineers. But after a term of AI-generated slides being read, at times, by an AI voiceover, James said he had lost faith in the programme and the people running it, worrying he had "used up two years" of his life on a course that had been done "in the cheapest way possible". "If we handed in stuff that was AI-generated, we would be kicked out of the uni, but we're being taught by an AI," said James during a confrontation with his lecturer recorded as a part of the course in October 2024. James and other students confronted university officials multiple times about the AI materials. But the university appears to still be using AI-generated materials to teach the course.

Large Language Models (LLMs) have advanced rapidly in recent years. One application of LLMs is to support student learning in educational settings. However, prior work has shown that LLMs still struggle to answer questions accurately within university-level computer science courses. In this work, we investigate how incorporating university course materials can enhance LLM performance in this setting. A key challenge lies in leveraging diverse course materials such as lecture slides and transcripts, which differ substantially from typical textual corpora: slides also contain visual elements like images and formulas, while transcripts contain spoken, less structured language. We compare two strategies, Retrieval-Augmented Generation (RAG) and Continual Pre-Training (CPT), to extend LLMs with course-specific knowledge. For lecture slides, we further explore a multi-modal RAG approach, where we present the retrieved content to the generator in image form. Our experiments reveal that, given the relatively small size of university course materials, RAG is more effective and efficient than CPT. Moreover, incorporating slides as images in the multi-modal setting significantly improves performance over text-only retrieval. These findings highlight practical strategies for developing AI assistants that better support learning and teaching, and we hope they inspire similar efforts in other educational contexts.

There is an increasing imperative to integrate programming platforms within AI frameworks to enhance educational tasks for both teachers and students. However, commonly used platforms such as Code.org, Scratch, and Snap fall short of providing the desired AI features and lack adaptability for interdisciplinary applications. This study explores how educational platforms can be improved by incorporating AI and analytics features to create more effective learning environments across various subjects and domains. We interviewed 8 K-12 teachers and asked their practices and needs while using any block-based programming (BBP) platform in their classes. We asked for their approaches in assessment, course development and expansion of resources, and student monitoring in their classes. Thematic analysis of the interview transcripts revealed both commonalities and differences in the AI tools needed between the STEM and non-STEM groups. Our results indicated advanced AI features that could promote BBP platforms. Both groups stressed the need for integrity and plagiarism checks, AI adaptability, customized rubrics, and detailed feedback in assessments. Non-STEM teachers also emphasized the importance of creative assignments and qualitative assessments. Regarding resource development, both AI tools desired for updating curricula, tutoring libraries, and generative AI features. Non-STEM teachers were particularly interested in supporting creative endeavors, such as art simulations. For student monitoring, both groups prioritized desktop control, daily tracking, behavior monitoring, and distraction prevention tools. Our findings identify specific AI-enhanced features needed by K-12 teachers across various disciplines and lay the foundation for creating more efficient, personalized, and engaging educational experiences.

While large language models (LLMs) challenge conventional methods of teaching and learning, they present an exciting opportunity to improve efficiency and scale high-quality instruction. One promising application is the generation of customized exams, tailored to specific course content. There has been significant recent excitement on automatically generating questions using artificial intelligence, but also comparatively little work evaluating the psychometric quality of these items in real-world educational settings. Filling this gap is an important step toward understanding generative AI's role in effective test design. In this study, we introduce and evaluate an iterative refinement strategy for question generation, repeatedly producing, assessing, and improving questions through cycles of LLM-generated critique and revision. We evaluate the quality of these AI-generated questions in a large-scale field study involving 91 classes -- covering computer science, mathematics, chemistry, and more -- in dozens of colleges across the United States, comprising nearly 1700 students. Our analysis, based on item response theory (IRT), suggests that for students in our sample the AI-generated questions performed comparably to expert-created questions designed for standardized exams. Our results illustrate the power of AI to make high-quality assessments more readily available, benefiting both teachers and students.

Rising seniors are the last class of students who remember high school before ChatGPT. But only just barely: OpenAI's chatbot was released months into their freshman year. Ever since then, writing essays hasn't required, well, writing. By the time these students graduate next spring, they will have completed almost four full years of AI high school. Gone already are the days when using AI to write an essay meant copying and pasting its response verbatim.

Retrieval practice is a well-established pedagogical technique known to significantly enhance student learning and knowledge retention. However, generating high-quality retrieval practice questions is often time-consuming and labor intensive for instructors, especially in rapidly evolving technical subjects. Large Language Models (LLMs) offer the potential to automate this process by generating questions in response to prompts, yet the effectiveness of LLM-generated retrieval practice on student learning remains to be established. In this study, we conducted an empirical study involving two college-level data science courses, with approximately 60 students. We compared learning outcomes during one week in which students received LLM-generated multiple-choice retrieval practice questions to those from a week in which no such questions were provided. Results indicate that students exposed to LLM-generated retrieval practice achieved significantly higher knowledge retention, with an average accuracy of 89%, compared to 73% in the week without such practice. These findings suggest that LLM-generated retrieval questions can effectively support student learning and may provide a scalable solution for integrating retrieval practice into real-time teaching. However, despite these encouraging outcomes and the potential time-saving benefits, cautions must be taken, as the quality of LLM-generated questions can vary. Instructors must still manually verify and revise the generated questions before releasing them to students. K eywords Retrieval practice large language models generative AI student learning multiple-choice questions STEM education data science higher education 1 Introduction Retrieval practice [30], frequently termed the "testing effect" [3], is a teaching technique that has been extensively studied. Empirical evidence has clearly indicated its ability to enhance long-term memory retention and overall learning [29, 3, 24, 27, 18, 2]. This technique involves the active recall of information from memory, a process that inherently strengthens neural connections and improves the future retrieval of that information.

The rise of AI, especially Large Language Models, presents challenges and opportunities to integrate such technology into the classroom. AI has the potential to revolutionize education by helping teaching staff with various tasks, such as personalizing their teaching methods, but it also raises concerns, for example, about the degradation of student-teacher interactions and user privacy. Based on interviews with teaching staff, this paper introduces INSIGHT, a proof of concept to combine various AI tools to assist teaching staff and students in the process of solving exercises. INSIGHT has a modular design that allows it to be integrated into various higher education courses. We analyze students' questions to an LLM by extracting keywords, which we use to dynamically build an FAQ from students' questions and provide new insights for the teaching staff to use for more personalized face-to-face support. Future work could build upon INSIGHT by using the collected data to provide adaptive learning and adjust content based on student progress and learning styles to offer a more interactive and inclusive learning experience.

We introduce AI University (AI-U), a flexible framework for AI-driven course content delivery that adapts to instructors' teaching styles. At its core, AI-U fine-tunes a large language model (LLM) with retrieval-augmented generation (RAG) to generate instructor-aligned responses from lecture videos, notes, and textbooks. Using a graduate-level finite-element-method (FEM) course as a case study, we present a scalable pipeline to systematically construct training data, fine-tune an open-source LLM with Low-Rank Adaptation (LoRA), and optimize its responses through RAG-based synthesis. Our evaluation - combining cosine similarity, LLM-based assessment, and expert review - demonstrates strong alignment with course materials. We also have developed a prototype web application, available at https://my-ai-university.com, that enhances traceability by linking AI-generated responses to specific sections of the relevant course material and time-stamped instances of the open-access video lectures. Our expert model is found to have greater cosine similarity with a reference on 86% of test cases. An LLM judge also found our expert model to outperform the base Llama 3.2 model approximately four times out of five. AI-U offers a scalable approach to AI-assisted education, paving the way for broader adoption in higher education. Here, our framework has been presented in the setting of a class on FEM - a subject that is central to training PhD and Master students in engineering science. However, this setting is a particular instance of a broader context: fine-tuning LLMs to research content in science.

Student simulation supports educators to improve teaching by interacting with virtual students. However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data. To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time. Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors. Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data. Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a ''digital twin'' for online education.

We describe the development of a one-credit course to promote AI literacy at The University of Texas at Austin. In response to a call for the rapid deployment of class to serve a broad audience in Fall of 2023, we designed a 14-week seminar-style course that incorporated an interdisciplinary group of speakers who lectured on topics ranging from the fundamentals of AI to societal concerns including disinformation and employment. University students, faculty, and staff, and even community members outside of the University, were invited to enroll in this online offering: The Essentials of AI for Life and Society. We collected feedback from course participants through weekly reflections and a final survey. Satisfyingly, we found that attendees reported gains in their AI literacy. We sought critical feedback through quantitative and qualitative analysis, which uncovered challenges in designing a course for this general audience. We utilized the course feedback to design a three-credit version of the course that is being offered in Fall of 2024. The lessons we learned and our plans for this new iteration may serve as a guide to instructors designing AI courses for a broad audience.