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Abstract The recent development and use of generative AI (GenAI) has signaled a significant shift in research activities such as brainstorming, proposal writing, dissemination, and even reviewing. This has raised questions about how to balance the seemingly productive uses of GenAI with ethical concerns such as authorship and copyright issues, use of biased training data, lack of transparency, and impact on user privacy. To address these concerns, many Higher Education Institutions (HEIs) have released institutional guidance for researchers. To better understand the guidance that is being provided we report findings from a thematic analysis of guidelines from thirty HEIs in the United States that are classified as R1 or “very high research activity.” We found that guidance provided to researchers: (1) asks them to refer to external sources of information such as funding agencies and publishers to keep updated and use institutional resources for training and education; (2) asks them to understand and learn about specific GenAI attributes that shape research such as predictive modeling, knowledge cutoff date, data provenance, and model limitations, and educate themselves about ethical concerns such as authorship, attribution, privacy, and intellectual property issues; and (3) includes instructions on how to acknowledge sources and disclose the use of GenAI, how to communicate effectively about their GenAI use, and alerts researchers to long term implications such as over reliance on GenAI, legal consequences, and risks to their institutions from GenAI use. Overall, guidance places the onus of compliance on individual researchers making them accountable for any lapses, thereby increasing their responsibility. 

Learning outcomes are clear and concise statements that describe what students should be able to do or know at the end of a particular course. These statements are crucial in instructional planning, curriculum development, and assessment of student progress and learning. Although there is no universal guidance on how to develop learning outcomes, Bloom’s taxonomy is one widely used framework that helps instructors develop outcomes that reflect different levels of thinking, from basic remembering to creative problem-solving. This study investigates the potential of generative AI, specifically GPT-4, in classifying course learning outcomes according to their respective cognitive levels within the revised Bloom’s taxonomy. To assess the effectiveness of GenAI, we conducted a comparative study using a dataset of 1000 annotated learning outcomes. We tested multiple prompt engineering strategies, including zero-shot, few-shot, chain-of-thought, rhetorical situation, and multiple binary questions, leveraging GPT-4. Classification performance was evaluated using accuracy, Cohen’s κ, and F1-score. The results indicate that the prompt incorporating rhetorical context and domain-specific knowledge achieved the highest classification performance, while the multiple binary question approach underperformed even compared to the zero-shot method. Furthermore, we compared the best-performing prompting strategy with a state-of-the-art classification model, BERT. Although the fine-tuned BERT model showed superior performance, prompt-based classification exhibited moderate to substantial agreement with expert annotations. Overall, this article demonstrates the potential of leveraging large language models to advance both theoretical understanding and practical application within the field of education and natural language processing. 

Providing rich, constructive feedback to students is essential for supporting and enhancing their learning. Recent advancements in Generative Artificial Intelligence (AI), particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback, effectively making abundant a resource that has historically been scarce and costly. From a technical perspective, this approach is now feasible due to breakthroughs in AI and Natural Language Processing (NLP). While the potential educational benefits are compelling, implementing these technologies also introduces a host of ethical considerations that must be thoughtfully addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a “tyranny of the majority”, where the diverse needs of minority or unique learners are overlooked, as they may be harder to systematize and less straightforward to accommodate. Ensuring inclusivity and equity in AI-generated feedback, therefore, becomes a critical aspect of responsible AI implementation in education. The process of developing machine learning models that produce valuable, personalized, and authentic feedback also requires significant input from human domain experts. Decisions around whose expertise is incorporated, how it is captured, and when it is applied have profound implications for the relevance and quality of the resulting feedback. Additionally, the maintenance and continuous refinement of these models are necessary to adapt feedback to evolving contextual, theoretical, and student-related factors. Without ongoing adaptation, feedback risks becoming obsolete or mismatched with the current needs of diverse student populations. Addressing these challenges is essential not only for ethical integrity but also for building the operational trust needed to integrate AI-driven systems as valuable tools in contemporary education. Thoughtful planning and deliberate choices are needed to ensure that these solutions truly benefit all students, allowing AI to support an inclusive and dynamic learning environment. 

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to wholehearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communicators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation. 

Generative artificial intelligence (GenAI) is increasingly becoming a part of work practices across the technology industry and being used across a range of industries. This has necessitated the need to better understand how GenAI is being used by professionals in the field so that we can better prepare students for the workforce. An improved understanding of the use of GenAI in practice can help provide guidance on the design of GenAI literacy efforts including how to integrate it within courses and curriculum, what aspects of GenAI to teach, and even how to teach it. This paper presents a field study that compares the use of GenAI across three different functions - product development, software engineering, and digital content creation - to identify how GenAI is currently being used in the industry. This study takes a human augmentation approach with a focus on human cognition and addresses three research questions: how is GenAI augmenting work practices; what knowledge is important and how are workers learning; and what are the implications for training the future workforce. Findings show a wide variance in the use of GenAI and in the level of computing knowledge of users. In some industries GenAI is being used in a highly technical manner with deployment of fine-tuned models across domains. Whereas in others, only off-the-shelf applications are being used for generating content. This means that the need for what to know about GenAI varies, and so does the background knowledge needed to utilize it. For the purposes of teaching and learning, our findings indicated that different levels of GenAI understanding needs to be integrated into courses. From a faculty perspective, the work has implications for training faculty so that they are aware of the advances and how students are possibly, as early adopters, already using GenAI to augment their learning practices. 

The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). In response, HEIs focused on regulating its use, particularly among students, before shifting towards advocating for its productive integration within teaching and learning. Since then, many HEIs have increasingly provided policies and guidelines to direct GenAI. This paper presents an analysis of documents produced by 116 US universities classified as as high research activity or R1 institutions providing a comprehensive examination of the advice and guidance offered by institutional stakeholders about GenAI. Through an extensive analysis, we found a majority of universities (N = 73, 63%) encourage the use of GenAI, with many offering detailed guidance for its use in the classroom (N = 48, 41%). Over half the institutions provided sample syllabi (N = 65, 56%) and half (N = 58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their teaching. Notably, the majority of guidance focused on writing activities focused on writing, whereas references to code and STEM-related activities were infrequent, and often vague, even when mentioned (N = 58, 50%). Based on our findings we caution that guidance for faculty can become burdensome as policies suggest or imply substantial revisions to existing pedagogical practices. 

Since the release of ChatGPT in 2022, Generative AI (GenAI) is increasingly being used in higher education computing classrooms across the United States. While scholars have looked at overall institutional guidance for the use of GenAI and reports have documented the response from schools in the form of broad guidance to instructors, we do not know what policies and practices instructors are actually adopting and how they are being communicated to students through course syllabi. To study instructors' policy guidance, we collected 98 computing course syllabi from 54 R1 institutions in the U.S. and studied the GenAI policies they adopted and the surrounding discourse. Our analysis shows that 1) most instructions related to GenAI use were as part of the academic integrity policy for the course and 2) most syllabi prohibited or restricted GenAI use, often warning students about the broader implications of using GenAI, e.g. lack of veracity, privacy risks, and hindering learning. Beyond this, there was wide variation in how instructors approached GenAI including a focus on how to cite GenAI use, conceptualizing GenAI as an assistant, often in an anthropomorphic manner, and mentioning specific GenAI tools for use. We discuss the implications of our findings and conclude with current best practices for instructors. 

The explosion of AI across all facets of society has given rise to the need for AI education across domains and levels. AI literacy has become an important concept in the current technological landscape, emphasizing the need for individuals to acquire the necessary knowledge and skills to engage with AI systems. This systematic review examined 47 articles published between 2019 and 2023, focusing on recent work to capture new insights and initiatives given the burgeoning of the literature on this topic. In the initial stage, we explored the dataset to identify the themes covered by the selected papers and the target population for AI literacy efforts. We identified that the articles broadly contributed to one of the following themes: a) conceptualizing AI literacy, b) prompting AI literacy efforts, and c) developing AI literacy assessment instruments. We also found that a range of populations, from pre-K students to adults in the workforce, were targeted. In the second stage, we conducted a thorough content analysis to synthesize six key constructs of AI literacy: Recognize, Know and Understand, Use and Apply, Evaluate, Create, and Navigate Ethically. We then applied this framework to categorize a range of empirical studies and identify the prevalence of each construct across the studies. We subsequently review assessment instruments developed for AI literacy and discuss them. The findings of this systematic review are relevant for formal education and workforce preparation and advancement, empowering individuals to leverage AI and drive innovation.