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.

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

CHI ’25, Yokohama, Japan

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.

In today's data-driven world, the importance of data literacy is paramount. However, software engineering education has not adequately addressed integrating comprehensive data science curricula, leaving students ill-equipped for the future of artificial intelligence (AI), which is built on the foundations of data science. This gap is exacerbated by the lack of tailored courses and the intimidating nature of existing tools for begin-ners. Consequently, students often miss out on essential skills like data cleanup, real-world application of machine learning (ML) algorithms, and the integration of big data in software products. This paper addresses these challenges by proposing a novel approach to applied data science for software engineering students. We argue for a shift from traditional algorithm-focused teaching to a curriculum emphasizing real-world problem-solving, lever-aging data science techniques. By empowering students to define and tackle their own data-driven projects, we aim to increase motivation, enhance data literacy, and instill a data-thinking mindset in future software engineers to prepare them for the AI world. Overall, this paper contributes to the advancement of software engineering education for young learners by offering a comprehensive framework, the data action educational framework (DAEF), and a data science toolkit that enables DAEF by empowering learners to create original data-driven mobile apps.

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.

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.

This paper contributes to the growing body of research that attempts to measure online, informal learning. We analyze skill progression in MIT App Inventor, an informal online learning environment with over 5 million users and 15.9 million projects/apps created. Our objective is to understand how people learn computational thinking concepts while creating mobile applications with App Inventor. In particular, we are interested in the relationship between the progression of skill in using App Inventor functionality and in using computational thinking concepts as learners create more apps. We model skill progression along two dimensions: breadth and depth of capability. Given a sample of 10,571 random users who have each created at least 20 apps, we analyze the relationship between demonstrating domain-specific skills by using App Inventor functionality and generalizable skills by using computational thinking concepts. Our findings indicate that domain-specific and generalizable skills progress similarly; there is a common pattern of expanding breadth of capability by using new skills over the first 10 projects, then developing depth of capability by using previously introduced skills to build more sophisticated apps.

Many smartphone apps collect personal information used for a variety of purposes. Users, however, are often unaware of this kind of access even though they must grant the required permissions upon app installation. We have identified three reasons for this unawareness. First, relevant permissions c

We review current technical and social barriers to COPPA compliance for popular online services aimed at children. We show that complying with COPPA has proven difficult for developers, even when a genuine attempt was made. We investigate reasons for this lack of compliance and identify common causes: specifically, difficulties obtaining verifiable parental control as well as supply mechanisms for parents to understand, review, grant access and monitor collection of their children's personal data. Unless part of online services, mobile apps do not need to comply with COPPA.

SIGCSE members love the beauty of computational thinking. They know the joy of bringing those ideas to young people. That love for computational thinking entails respect for the computational values that empower people in the digital world. For academics, those values have been central to the flowering of computing as an intellectual endeavor. Today, those values are increasingly threatened by stresses from both within and outside academia: squabbles over who owns academic work, increasingly stringent and overreaching intellectual property laws, and the replacement of open computing platforms by closed applications and walled-garden application markets.

App Inventor for Android is a visual blocks language for building mobile apps. Like Scratch, the language's drag-and-drop blocks interface significantly lowers the barrier to entry. Beginners can immediately build apps that interface with mobile technology (e.g., GPS, Text-to-speech, SMS Texting) and build apps that have a real-world impact. App Inventor has great potential for increasing interest in programming and attracting women and other underrepresented groups to computer science. Students learn by tinkering with their most beloved devices, phones and tablets, and even novices can create apps in an exciting and intuitive environment. App Inventor is relevant to teachers from middle school through the university level who are interested in a highly motivating method of teaching programming. In this BoF, we will discuss the language, its future in K-12 and university education, and its new home at the MIT Center for Mobile Learning.

This paper discusses the institutional framework and the strategic decisions the led the launch of several major educational technology initiatives at the Massachusetts Institute of Technology (MIT) between 1999 and 2003. It describes how MIT's central administration provided strategic support and coordination for large educational technology programs and traces how strategies evolved as work progressed through 2003 to a point where major projects had been launched and were ready to proceed as ongoing concerns. The history recounted here provides a snapshot of a world-class university's confronting the changing environment for higher education engendered by information technology at beginning of the twenty-first century.

Lower bounds on the interprocessor communication required for computing a differentiable real-valued function in a distributed network are derived. These bounds are independent of the network interconnection configuration, and they impose no assumptions other than differentiability constraints on the computations performed by individual processors. As a sample application, lower bounds on information transfer in the distributed computation of some-typical matrix operations are exhibited.

We derive a lower bound on the interprocessor information transfer required for computing a function in a distributed network configuration. The bound is expressed in terms of the function's derivatives, and we use it to exhibit functions whose computation requires a great deal of interprocess communication. As a sample application, we give lower bounds on information transfer in the distributed computation of some typical matrix operations. Traditional measures of computational complexity, such as the number of primitive operations or memory cells required to compute functions, do not form an adequate framework for assessing the complexity of computations carried out in distributed networks. Even in the relatively straightforward situation of memoryless processors arranged in highly structured configurations, Gentleman [4] has demonstrated that data movement, rather than arithmetic operations, can often be the significant factor in the performance of parallel computations. And for the more general kinds of distributed processing, involving arbitrary network configurations and distributed data bases, the situation is correspondingly more complex. This paper addresses the problem of measuring computational complexity in terms of the interprocess communication required when a computation is distributed among a number of processors. More precisely, we model the distributed computation of functions which depend upon large amounts of data by assuming that the data is partitioned into disjoint subsets, and that a processor is assigned to each subset. Each processor (which we can think of as a node in a computational network) computes some values based on its own "local" data, and transmits these values to other processors, which are able to use them in subsequent local comutations. This "compute locally and share information" procedure is repeated over and over until finally some (predetermined) processor outputs the value of the desired function. In measuring the complexity of such computations we will be concerned, not with the individual local computations, but rather with the total information transfer, i.e., the total number of values which must be transmitted between processors. We derive a lower bound on the total information transfer required for computing a function in a distributed network. The bound is expressed in terms of the function's derivatives, and we use it to exhibit functions whose computation requires a great deal of interprocess communicaion. As a sample application, we give lower bounds on information transfer in the distributed computation of some typical matrix operations.