Collaboration is a cornerstone of 21st-century learning, yet teachers continue to face challenges in supporting productive peer interaction. Emerging generative AI tools offer new possibilities for scaffolding collaboration, but their role in mediating in-person group work remains underexplored, especially from the perspective of educators. This paper presents findings from an exploratory qualitative study with 33 K12 teachers who interacted with Phoenix, a voice-based conversational agent designed to function as a near-peer in face-to-face group collaboration. Drawing on playtesting sessions, surveys, and focus groups, we examine how teachers perceived the agent's behavior, its influence on group dynamics, and its classroom potential. While many appreciated Phoenix's capacity to stimulate engagement, they also expressed concerns around autonomy, trust, anthropomorphism, and pedagogical alignment. We contribute empirical insights into teachers' mental models of AI, reveal core design tensions, and outline considerations for group-facing AI agents that support meaningful, collaborative learning.

Enabling AI literacy in the general population at scale is a complex challenge requiring multiple stakeholders and institutions collaborating together. Industry and technology companies are important actors with respect to AI, and as a field, we have the opportunity to consider how researchers and companies might be partners toward shared goals. In this symposium, we focus on a collection of partnership projects that all involve Google and all address AI literacy as a comparative set of examples. Through a combination of presentations, commentary, and moderated group discussion, the session, we will identify (1) at what points in the life cycle do research, practice, and industry partnerships clearly intersect; (2) what factors and histories shape the directional focus of the partnerships; and (3) where there may be future opportunities for new configurations of partnership that are jointly beneficial to all parties.

Initial discussion of AI literacy assessment has focused on competency frameworks and learning standards rather than materials for classroom use. Responsible AI for Computational Action (RAICA), a constructionist AI curriculum for middle and high school students, includes assessment materials to support teachers with the evaluation of student AI literacy competencies in their classrooms. These materials include exit tickets used as formative assessments at the end of each lesson and both teacher and student-facing rubrics. After beta-testing a module of the curriculum with nine teachers and 282 students, we reviewed teacher usage data and feedback as well as student responses. The review process surfaced a number of improvements to the materials to better align them with classroom teaching practice. These included clarifying language and adding visual scaffolds. We present the assessment materials and iterative design process used to bridge the gap between the theoretical AI literacy competencies and their practical implementation in classrooms.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu

Ethical thinking and reasoning is considered a core component of artificial intelligence (AI) literacy. However, there is a lack of strategies and assessments to promote and measure students’ ethical thinking in AI, particularly in K-12 education. In this paper, we discuss how RAICA, a project-based AI literacy curriculum integrates ethics into its framework and instructional resources. We employ a mixed-methods convergent design to obtain different yet complementary data on ethical thinking as an outcome mediated by diverse RAICA materials. Data analysis revealed that teachers utilized a variety of instructional strategies to foster students’ ethical thinking and that students actively engaged in ethical thinking activities, resulting in a developing understanding of stakeholders and potential benefits/harms of AI. Our work makes a key contribution to AI education by providing empirical evidence to support mechanisms for integration and assessment of ethical thinking within AI literacy curricula.

The emergence of generative AI, particularly large language models (LLMs), has opened the door for student-centered and active learning methods like project-based learning (PBL). However, PBL poses practical implementation challenges for educators around project design and management, assessment, and balancing student guidance with student autonomy. The following research documents a co-design process with interdisciplinary K-12 teachers to explore and address the current PBL challenges they face. Through teacher-driven interviews, collaborative workshops, and iterative design of wireframes, we gathered evidence for ways LLMs can support teachers in implementing high-quality PBL pedagogy by automating routine tasks and enhancing personalized learning. Teachers in the study advocated for supporting their professional growth and augmenting their current roles without replacing them. They also identified affordances and challenges around classroom integration, including resource requirements and constraints, ethical concerns, and potential immediate and long-term impacts. Drawing on these, we propose design guidelines for future deployment of LLM tools in PBL.

Part of a university initiative supporting responsible AI for social empowerment and education, the project-based RAICA (Responsible AI for Computational Action) curriculum supports middle/high school learners and novice AI literacy teachers use AI creatively for good. This paper offers a rare example of design-based implementation research (DBIR) in AI education across widely varied contexts, provides fine grain implementation data that contributes to a foundation for evaluating effectiveness and expanding access. We present a novel approach to analyzing fidelity of implementation data from RAICA’s computer vision module beta-test. Twelve educators working with ~282 students across nine pilot sites in four countries used a bespoke fidelity of implementation data collection tool (pre-made comment prompts in a Google Docs version of the teacher guide) to provide 236 qualitative responses about AI literacy and responsible design activities, plus 111 ordinal ratings of embedded teacher supports. Analyses revealed that while the curriculum was generally implemented as designed, educators frequently made modifications. Although most changes produced practical insights for improved curriculum design, others helped the design team anticipate and prevent changes that could obscure learning objectives and hinder outcomes. We discuss the pedagogical, design, and research implications of these findings for effective AI teaching/learning in diverse settings.

In November of 2022, a Silicon Valley company launched an invention that could complete students' homework for them. Available only to subscribers at first, by the spring of 2023 OpenAI's ChatGPT-3.5 was available to millions of students. As of January 2023, anyone with . . .

Data science is emerging as a crucial 21st-century competence, influencing professional practices from citing evidence when advocating for social change to developing artificial intelligence (AI) models. For middle and high school students, data science can put formerly decontextualized subjects into real-world scenarios. Many existing curricula, however, lack authenticity and personal relevance for students. A critique of data science courseware cites the lack of "author proximity," in which students do not contribute to the data's production or see their personal experiences reflected in the data. This paper introduces a novel data science curriculum to scaffold middle and high school students in undertaking real-world data science practices. Through project-based learning modules, the curriculum engages students in investigating solutions to community-based problems through visualization and analysis of live sensor data and public data sets. Materials include formative assessments to help educators (especially those from non-math and computing backgrounds) measure their students' abilities to identify statistical patterns, critically evaluate data biases, and make predictions. As we pilot and co-design with teachers, we will look closely at whether the curriculum's resources can successfully support non-technical practitioners engaging in an integrated curriculum.

Text-to-image generation (TTIG) technologies are Artificial Intelligence (AI) algorithms that use natural language algorithms in combination with visual generative algorithms. TTIG tools have gained popularity in recent months, garnering interest from non-AI experts, including educators and K-12 students. While they have exciting creative potential when used by K-12 learners and educators for creative learning, they are also accompanied by serious ethical implications, such as data privacy, spreading misinformation, and algorithmic bias. Given the potential learning applications, social implications, and ethical concerns, we designed 6-hour learning materials to teach K-12 teachers from diverse subject expertise about the technical implementation, classroom applications, and ethical implications of TTIG algorithms. We piloted the learning materials titled “Demystify text-to-image generative tools for K-12 educators" with 30 teachers across two workshops with the goal of preparing them to teach about and use TTIG tools in their classrooms. We found that teachers demonstrated a technical, applied and ethical understanding of TTIG algorithms and successfully designed prototypes of teaching materials for their classrooms.

Teaching young people about artificial intelligence (A.I.) is recognized globally as an important education effort by organizations and programs such as UNICEF, OECD, Elements of A.I., and AI4K12. A common theme among K-12 A.I. education programs is teaching how A.I. can impact society in both positive and negative ways. We present an effective tool that teaches young people about the societal impact of A.I. that goes one step further: empowering K-12 students to use tools and frameworks to create socially responsible A.I. The computational action process is a curriculum and toolkit that gives students the lessons and tools to evaluate positive and negative impacts of A.I. and consider how they can create beneficial solutions that involve A.I. and computing technology. In a human-subject research study, 101 U.S. and international students between ages 9 and 18 participated in a one-day workshop to learn and practice the computational action process. Pre-post questionnaires measured on the Likert scale students’ perception of A.I. in society and students' desire to use A.I. in their projects. Analysis of the results shows that students who identified as female agreed more strongly with having a concern about the impacts of A.I. than those who identified as male. Students also wrote open-ended responses to questions about what socially responsible technology means to them pre- and post-study. Analysis shows that post-intervention, students were more aware of ethical considerations and what tools they can use to code A.I. responsibly. In addition, students engaged actively with tools in the computational action toolkit, specifically the novel impact matrix, to describe the positive and negative impacts of A.I. technologies like facial recognition. Students demonstrated breadth and depth of discussion of various A.I. technologies' far-reaching positive and negative impacts. These promising results indicate that the computational action process can be a helpful addition to A.I. education programs in furnishing tools for students to analyze the effects of A.I. on society and plan how they can create and use socially responsible A.I.

We present the Day of AI, an innovative program for K-12 educators around the world to bring AI literacy curricula to their local classrooms and communities, all for free. Our open teacher-facing materials, student-facing materials, and supporting technologies were developed to address key challenges in empowering teachers to bring multi-disciplinary, hands-on AI literacy learning opportunities to their students in the face of limited pedagogical practices, curriculum, and resources in their community. We designed a modular, 4-hour format for multiple grade bands, spanning upper elementary through high school, to bring AI literacy to K-12 classrooms at scale. Student learning objectives included: demystifying how AI works, analyzing AI applications, and thinking critically about the ethical use of AI and its societal implications. Our research study utilized a sequential mixed methods approach to design and evaluate the effectiveness of our curriculum and professional development resources to support teachers in bringing the curricula to their classrooms. A total of 108 teachers from over twenty countries participated in our study, who collectively taught the curricula to over 7,000 students. Quantitative and qualitative findings suggest that teachers were well supported by our teacher-facing materials and professional development training. Teachers also positively rated students’ engagement and AI literacy knowledge gains with the curricula.

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 8404-8412ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.2183Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain

Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semi-structured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

As artificial intelligence involves and shapes personal and professional lives, there is a critical need to nurture and prepare AI-enabled problem-solvers. FutureMakers is designed as a six-week program that introduces foundational knowledge and essential skills to develop innovative solutions with AI responsibly. Our study utilized a convergent mixed-method design to evaluate the impact of the FutureMakers program on students' learning outcomes and shifting perspectives on AI. Quantitative data showed a shift in students' AI literacy with a large effect size. Qualitative data, based on student interviews, showed an awareness of an ethical engineering design process in applying technical skills to solve real-world problems. The program showed the impact of the computational action approach to tackle authentic challenges.

MIT Aptly is a tool that uses the technology of large language models to automatically generate mobile apps from written or spoken natural language descriptions. Similar to Github’s Copilot, it is based on OpenAI’s Codex, a specially tuned version of GPT-3. Aptly lets people create programs without requiring any use of coding or knowledge of programming. For example, one can tell Aptly by speaking or typing: Make an app with a text box, a list of six languages and a button that says “translate.” When the button is clicked, translate the text into the selected language and show the translation. The result is a complete functioning app for Android or iPhone. The app has a field for user input and six buttons labeled English, Spanish, French, German, Italian, Japanese. Pressing one of the buttons translates the input to the corresponding language. Aptly’s app generation is more than just a syntactic transformation of the input text. Aptly draws upon a large body of code with which it has been trained to provide a context for its app creation. In the above example, Aptly has independently chosen the six languages, something that was not specified in the input text. As most large language models do, Aptly’s performance depends on the input given to OpenAI’s Codex. These inputs are referred to as prompts . Aptly crafts a prompt by providing a set of example pairs (a textual description of an example app and its corresponding code) along with the description of the desired app. Such prompt engineering is referred to as few-shot prompts. In order to optimize Aptly’s performance, when selecting example pairs, we choose the ones that are semantically close to the description of the desired app. The prospect of no-code platforms is currently sparking considerable ferment in enterprises concerned with professional programming careers. Similarly, Aptly poses challenges for research in computational thinking education for K-12 students. Much of the present-day curriculum emphasizes implementing computational artifacts using text-based coding with Python or block-based coding with Scratch or App Inventor. What will be the foundations for that curriculum when tools like Aptly are common and the transition from ideas to running programs can be accomplished automatically? This presentation will demonstrate Aptly’s preliminary performance and review its implementation, which incorporates OpenAI Codex, Amazon Alexa and MIT App Inventor.

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 1804-1814ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.0460Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain

The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of experiential learning in one class, while also evaluating the learning performance of students who were taught with the conventional instruction, which was called the cycle of doing projects. Two factors, learning approach and gender, were taken into account. The results showed that females’ learning effectiveness was significantly better than that of males regardless of whether they used experiential learning or the conventional projects approach. Most of the males tended to be distracted from the conversational AI curriculum because they misbehaved during the conversational AI process. In particular, in their performance using the Voice User Interface with the conventional learning approach, the females outperformed the males significantly. The results of two-way ANCOVA revealed a significant interaction between gender and learning approach on computational thinking concepts. Females with the conventional learning approach of doing projects had the best computational thinking concepts in comparison with the other groups.