The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule-based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce students to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to classify higher-order objects. We discuss how this tool can be used as a "first step" to the more complex concept ideas of data representation and neural networks.

This research to practice full paper explores a new educational framework for AI-informed leadership and evaluates its curriculum and pedagogical approach through a novel, tailored, research instrument. Artificial Intelligence continues to rapidly transform many aspects of markets, solutions, and organizational culture across companies, agencies, and institutions in the public and private sectors. Within complex organizations, AI tools, technologies, and applications inform how leaders engage in strategy-making, management, operations, human resources, and professional education. Non-technical managers and executives are increasingly expected to lead teams to implement responsible AI solutions with the promise to improve efficiency, effectiveness, productivity, profitability, and more. AI is rapidly transforming organizational culture, requiring non-technical leaders to develop AI literacy and essential skills to lead teams in implementing responsible AI solutions. In the face of AI-driven change, business leaders need to be AI literate and develop their own essential skills, knowledge, procedures, and perspectives to successfully set vision and strategy to lead teams that can leverage AI to achieve inward-facing and outward-facing business goals. This presents challenges and opportunities to develop new pedagogical approaches and measures to prepare and assess business leaders' AI leadership skills - including understanding human-AI systems in the workplace and their responsible development and ethical use. There are also cultural and organizational behavior challenges in successfully adopting these new capabilities into a global and diverse human-AI workforce at scale. To advance these, we present an innovative hands-on AI leadership curriculum, where participants learn by making and team problem-solving, for United States Air Force (USAF) leaders to learn about AI and its responsible use in human-robot teaming with autonomous robots. We contribute new measures to assess their attitudinal shifts in AI leadership with respect to culture, mindsets, and ethics. We present a pilot study to evaluate our curriculum design and pedagogical approach to foster positive shifts in our AI leadership measures.

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.

Special education teachers’ perception and attitudes towards technology and design play a critical role in pedagogical practices. The study aims to explore changes in special education teachers’ attitudes and design beliefs through a co-design process with children. The initial pilot study focused on preparing special education teachers for effective integration of pedagogical agents into teaching and learning. The initial pilot study followed the mixed-method design and was guided by the following research question: In what ways the co-design process influenced teachers’ attitudes and design beliefs towards pedagogical agents through the co-design process with children? The preliminary results indicated by the end of the program that teachers’ attitudes towards pedagogical agents increased significantly with moderate effect sizes, which might contribute to the co-design process and interactions with pedagogical agents. Qualitative analysis based on teacher interviews, lesson projects, and field notes also suggested that the shifts in participant's attitudes and design beliefs are influenced by a variety of personal and contextual factors including i) The didactic use of pedagogical agents; ii) the usefulness of pedagogical agents for inclusive education; (iii) teachers’ attitudes about the role of the teacher as a designer, and (iv) leadership support to facilitate the immersive learning experience created through the interaction between the human and pedagogical agents.

This Research to Practice Full Paper presents an AI Education program. In January 2021 MIT entered into an agreement with the United States Air Force (USAF) and the Department of Defense (DoD) to design and offer a new educational research program focusing on Artificial Intelligence (AI) training. The goal of this collaboration is to design and advance educational research activities that promote maximum learning outcomes at scale for learners with diverse roles and educational backgrounds, ranging from Air Force and DoD personnel to the general public. This program is expected to offer different learning tracks addressing different groups of USAF employees based on their unique professional needs and backgrounds. The first pilot is currently underway and will provide the research team with data and insights that will inform the next iteration of the program, with the ultimate goal of formulating recommendations for the USAF and general public on how to reach large numbers of learners at scale in an optimum way. Currently, the program offers three different learning journeys for each of three different cohorts of USAF employees (i.e., leaders, developers, and users). These learning journeys span from online asynchronous and synchronous courses to in-person activities. Our research goals focus on exploring and understanding the learner experience via the study and analysis of AI content and curriculum, pedagogical approaches, learning modalities, and technological innovations to deliver learning experiences at scale. Key research activities involve evaluating a range of existing digital AI courses, mapping out the landscape of educational needs and competencies, and developing and piloting experiential learning experiences (to advance innovative technology-enabled training and learning technologies and methods). This paper discusses how preliminary research findings from this first pilot are informing the design and implementation of the next program iteration. The research provides insights that will benefit AI learners across the US while supporting the DoD’s objective to develop elite and world-class AI-ready services.

Motivated by the need to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who may have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The robot is built using a commercially available powered wheelchair modified to be controlled by head motion. Head motion is measured by tracking an egocentric camera mounted on the user’s head and faces outward. Compared with previous approaches to hands-free mobility, our system provides a more natural human robot interface because it enables the user to control the speed and direction of motion in a continuous fashion, as opposed to providing a small number of discrete commands. This article presents three usability studies, which were conducted on 37 subjects. The first two usability studies focus on comparing the proposed control method with existing solutions while the third study was conducted to assess the effectiveness of training subjects to operate the wheelchair over several sessions. A limitation of our studies is that they have been conducted with healthy participants. Our findings, however, pave the way for further studies with subjects with disabilities.