In this research-to-practice full paper, we present the third iteration of our educational framework that advances AI-informed leadership - a much-needed competency in this era of rapid AI transformation. Our study aimed to evaluate our proposed content and pedagogy and whether it can be made widely accessible to non-technical leaders. We focused on modifying our existing curriculum and research protocol based on the feedback and learnings from our previous workshops. Our current workshop is part of an AI-Education program developed by MIT aiming to provide AI education to the U.S Air Force, which is one of the largest organizations in the US. Previous two iterations of the workshop aimed to offer an introduction to AI to U.S Air Force leaders, while the current iteration's goals are twofold: offering the updated content to U.S Air Force leaders, and supporting their capacity building by preparing a cohort of workshop trainers, who will lead the future iterations of the program inhouse. We conducted the workshop with over 50 participants, including 10 facilitators who were trained to run future workshops internally. Our comprehensive educational materials, including fa-cilitator guides, student-facing documents, and digital resources, support this scalable model. The results demonstrate significant improvements in AI knowledge and leadership competencies, including AI mindset, culture, and ethos. Our findings affirm the effectiveness of experiential learning methodologies and underscore the potential for scalable, sustainable AI education within large organizations through the facilitators' feedback.

This work-in-progress innovative practice paper presents a novel approach to 1) extract tacit knowledge from expert trainers while they perform a task demo, 2) decrease the learner's cognitive load via the use of instructional videos portraying the variables at play during a task demonstration, and 3) define quantifiable metrics of expertise by extracting features that differentiate experts from novice practitioners. Implicit or tacit knowledge is know-how that experts develop with experience and is difficult to verbalize, formalize or explicitly transfer to others. For this reason, knowledge transfer from expert to apprentice is usually slow and inefficient. Our approach seeks to support knowledge transfer using technology-enhanced approaches. Here, we focus on extracting and describing exper-tise. We do so by instrumenting experts, trainees and their tools with sensors that can help structure and formalize knowledge. Our first application of this framework is on the knowledge transfer between an expert and novice glassblower. Glassblowing is well known for its crucial expert/apprentice relation and its slow learning rate due in part to the difficulties in verbal transfer of skills. Our framework seeks to capture relevant data while an expert glassblower demonstrates basic actions in a beginners glass blowing course. Our sensors collect eye-tracking activity, verbal demo instructions, pipe accelerometry, air infusion, scene video and muscle activity (EMG), which continuously monitor the expert, their explanations, the tools, and the glass piece. We bring together all the sensed data into instructional videos to be used by novice learners as supportive training material. We present preliminary results related to metrics of expertise and future steps towards gathering similar data from novices. This will help develop AI-based models to extract data-driven differences between experts and apprentices, which can be used as further instructional material. We will also present plans to test the instructional effectiveness of the developed videos and how our approach can be used in other training settings involving tacit knowledge transfer.

This innovative research paper presents a program that introduces entrepreneurship, innovation, and biomedical product development to engineering, computer science, and other STEM and non-STEM professionals to engage them into startup creation with the goals of preventing, diagnosing or treating substance use disorder (SUD), one of the US most pressing health and social challenges. For more than two decades, SUD has been affecting people from all ages, demographic and socio-economic groups in the US. The National Institute on Drug Abuse (NIDA), as the lead federal agency supporting scientific research on drug use, has identified that current SUD research is not properly translating into commercial solutions for SUD. Seeking alternatives, NIDA is fostering the entrepreneurial spirit of SUD researchers, so they are the ones directly offering SUD-focused technologies into the market. This paper presents the MIT SUD Ventures program, a NIDA-funded project focused on training multidisciplinary teams of SUD researchers, engineers, healthcare, and management experts on how to commercialize their discoveries with the support of government funding sources. We introduce the learner profile, the content and skills deemed necessary to support their entrepreneurial efforts, the curriculum implemented and the program structure. We share results related to the learner expectations, experiences, and outcomes of this innovative practice, as well as future steps for the 2024 cohort and impact evaluation. Finally, guided by our results, we discuss the positive value of teaching entrepreneurial skills to academics and researchers to bridge the gaps between basic and translational research. We also make a call for action for all STEM professionals, especially engineers, computer scientists and technologists, to employ their scholarship and research capacity to have a positive impact on solving the SUD epidemic, where they are most needed.

This innovative practice full paper presents a novel educational program that aims to improve work readiness of emerging talent around the world through remote, paid, global apprenticeships, and human skills training for both apprentices and managers. In the last decade, multiple technical and socioeconomic factors, along with the COVID-19 pandemic, have radically changed the job market, the way companies interact with their employees and customers, and how universities train their students. In response to these changes, we have identified three core aspects of the modern workforce that need attention from academic institutions and industry to promote a more diverse, inclusive, and stable working environment for entry-level and emerging talent, across the world: 1) remote work readiness, 2) real-life mentored learning (apprenticeships), and 3) manager and supervisor preparedness. Remote work, when properly implemented, has presented advantages and opportunities to students, workers, and companies: it improves performance and facilitates innovation through cross-pollination of ideas between diverse groups and gives opportunities to emerging talent globally. Internships and apprenticeships are mechanisms implemented to promote a smoother transition from academia to the workplace. Both internships and apprenticeships promote real-life work experience but differ in that the latter includes a predesigned learning experience guided by a mentor (a seasoned manager). Senior undergraduates are frequently ill-prepared to face the difficulties of work because of a disconnect between their academic training and the needs of a job. This is especially critical in engineering students, who focus mainly on technical skills, leaving behind human and professional skills necessary to thrive in the workplace. Internships and apprenticeships offer opportunities to bridge this gap, though several evaluation criteria must be defined and met to consider them successful. To enhance remote work readiness, as well as success in internship and apprenticeship programs, manager and supervisor preparedness is critical for properly guiding engineering students, apprentices, and entry-level employees in their first job experience. With these core concepts in mind and using the Agile Continuous Education (ACE) framework, The Intern Group (TIC) and MIT Open Learning (MIT OL) created the Global Apprenticeship Program (GAP), an initiative focused on bridging the gap between talent and opportunities around the world. The program aims to 1) increase apprenticeship performance and facilitate full-time employment for students and diverse emerging talent, at a global scale; and 2) support how companies successfully recruit, onboard, and retain emerging talent. Innovation in this approach lies in the particular focus placed on the apprentice-mentor (intern-manager) dyad, including tailored training for managers and supervisors. In this paper we present in detail the different programmatic components of the learning tracks. These consist of a variety of individual self-paced asynchronous learning activities, group learning synchronous workshops, community building and cultural exchange events, and a real-life mentored learning experience (apprenticeship). We conclude with implementation challenges, opportunities for improvement, and lessons learned regarding content, pedagogies, and technologies used throughout the program, and effect on participation and engagement.

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.

In this work, we leverage neural mechanisms of visual attention to improve the accuracy of a commercial eye tracker through the analysis of electroencephalography (EEG) waves. Gaze targets were rendered in a computer screen with imperceptible flickering stimuli (≥ 40Hz) that elicited attention-modulated steady-state visual evoked potentials (SSVEPs). Our hybrid system combines EEG and eye-tracking modalities to overcome accuracy limitations of the gaze-tracker alone. We integrate EEG and gaze data to efficiently exploit their complementary strengths driving a Bayesian probabilistic decoder that estimates the target gazed by the user. Our system’s performance was analyzed across the screen with varying target sizes, spacings and dataset epoch lengths, using data from 10 subjects. Overall, our hybrid approach improves the classification accuracy of the eye tracker alone for all target parameters and dataset epoch lengths in 11 units on average. The system shows a larger impact at peripheral screen regions where performance enhancement is maximal, reaching improvements of over 45 units. The findings of this work demonstrate that the intrinsic accuracy limitations of camera-based eye-trackers can be corrected with the integration of EEG data, and opens opportunities for gaze tracking applications with higher target granularity.

Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.