Victor R. Lee

1 IntroductionData are being produced in a range of contexts for a range of purposes, whether those are addressing the concerns of scientific inquiry, industry interests, or for informing governmen...

This study examines the evolving relationship between AI chatbots and academic integrity and students’ AI chatbot usage in high schools one and a half years after the release of ChatGPT. Through a comprehensive survey of students across six schools (N = 4,354) in the United States, we investigated students' self-reported cheating behaviors, patterns of AI use across different school-related tasks, and student perspectives on appropriate AI use in academic settings. Our findings revealed that overall cheating rates remain stable at 72.06%, consistent with historical baselines and prior studies, suggesting that AI availability has not changed overall cheating prevalence in high school. Additionally, more students reported using AI chatbots for support tasks like concept explanation and idea generation. Regarding students' reported preferences for allowing AI chatbots for school-related tasks, at this point, they still strongly supported using AI for conceptual understanding and brainstorming, and they maintained clear boundaries against using it for completing entire assignments. These findings suggest that while AI’s prevalence has not altered the patterns of academic integrity at schools, students' evolving perspectives on appropriate AI use provide valuable insights for schools and administrators integrating AI into traditional school settings.

AI literacy is increasingly discussed but does not have an agreed-upon definition or operationalization, specifically for curriculum and instructional design guidance. Different groups have tried to address this by producing framework documents that articulate key ideas and activities for AI literacy. In order to help characterize the broad set of AI literacy frameworks that are emerging, Lee and Long [1] proposed the User-Developer-Critic (UDC) perspective space model as a meta-framework. UDC posits that AI literacy frameworks exist in a space with emphases on the perspectives of users, developers, and critics. To illustrate one use of this meta-framework, this paper presents a keyword analysis of five framework documents relative to the three perspectives of UDC. In doing this analysis, some differences appear between framework documents, such as more or less emphasis on developer perspectives and ideas about the mechanics of AI technology. Additionally, critic perspectives are present, but do not appear to be dominant in any of the framework documents.

Interactive data learning tools provide explorable ways for students to build intuitions about data, data representations, and statistical parameters. However, these tools rely on visual consumption and are not accessible to blind and low vision (BLV) students. In this work, we investigate opportunities to leverage active exploration, enriched with multimodal feedback and embodied interaction, to foster an understanding of the relationships among individual data values, data representations, and statistical measures. We explore these opportunities in the form of an accessible learning platform that allows students to hear and feel how statistical measures are changing in real time as they construct and manipulate physicalized data representations. We introduced the platform to four teachers of students with visual impairments (TVIs) through a two-hour-long focus group. TVIs embraced the platform's exploratory nature and universality and recommended the consideration of additional auditory and texture-based interactions to enhance engagement.

In an age where Large Language Models (LLMs) expedite the generation of text, the skills for critically evaluating and creating meaningful text using these models are often lacking. To help classroom teachers address this, we introduce Prompty, a specialized teaching tool co-designed to facilitate both critical and effective use of LLMs. Prompty serves multiple learning goals: it allows students to critically evaluate text generated by LLMs, aids in their writing practice, and provides a deeper understanding of how LLMs function—all within a student-friendly environment secured by essential guardrails. Prompty was co-designed in collaboration with high school teachers as part of CRAFT, an initiative by Stanford University to promote AI literacy. It was pilot-tested in a high school English class to serve as an AI writing assistant, focusing on the critical evaluation of machine-generated text. This trial yielded preliminary evidence that attests to the tool's effectiveness in fulfilling its educational goals. The findings from the pilot study indicate that easy-to-use tools like Prompty have great potential. These tools can be adapted to fit the goals of individual teachers. They can help in achieving subject-specific learning goals while serving as an effective way to teach AI concepts in high school.