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1. Data-Related Ethics Issues in Technologies for Informal Professional Learning

   https://doi.org/10.1007/s40593-021-00259-x  

   Pammer-Schindler, Viktoria; Rosé, Carolyn (July 2021, International Journal of Artificial Intelligence in Education)

   null (Ed.)

   Abstract Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges. 

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2. A conceptual framework for human–AI hybrid adaptivity in education

   https://doi.org/10.1007/978-3-030-52237-7_20  

   Holstein K., Aleven V. (January 2020, Artificial Intelligence in Education. AIED 2020)

   Educational AI (AIEd) systems are increasingly designed and evaluated with an awareness of the hybrid nature of adaptivity in real-world educational settings. In practice, beyond being a property of AIEd systems alone, adaptivity is often jointly enacted by AI systems and human facilitators (e.g., teachers or peers). Despite much recent research activity, theoretical and conceptual guidance for the design of such human–AI systems remains limited. In this paper we explore how adaptivity may be shared across AIEd systems and the various human stakeholders who work with them. Based on a comparison of prior frameworks, which tend to examine adaptivity in AIEd systems or human coaches separately, we first synthesize a set of dimensions general enough to capture human–AI hybrid adaptivity. Using these dimensions, we then present a conceptual framework to map distinct ways in which humans and AIEd systems can augment each other’s abilities. Through examples, we illustrate how this framework can be used to characterize prior work and envision new possibilities for human–AI hybrid approaches in education. 

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3. Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms

   https://doi.org/10.1007/978-3-030-23204-7_14  

   Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent (June 2019, Proceedings, 20th International Conference on Artificial Intelligence in Education, AIED 2019)

   As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K-12 teachers and students. Using human-centered design methods rarely employed in AIED research, this work builds on prior findings to contribute: (1) an analysis of teacher and student feedback on 24 design concepts for systems that integrate human and AI instruction; and (2) participatory speed dating (PSD): a new variant of the speed dating design method, involving iterative concept generation and evaluation with multiple stakeholders. Using PSD, we found that teachers desire greater real-time support from AI tutors in identifying when students need human help, in evaluating the impacts of their own help-giving, and in managing student motivation. Meanwhile, students desire better mechanisms to signal help-need during class without losing face to peers, to receive emotional support from human rather than AI tutors, and to have greater agency over how their personal analytics are used. This work provides tools and insights to guide the design of more effective human–AI partnerships for K-12 education. 

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4. Technology ecosystem for orchestrating dynamic transitions between individual and collaborative AI-tutored problem solving

   https://doi.org/10.1007/978-3-031-11644-5_66  

   Yang, K.; Lu, Z.; Echeverria, V.; Sewall, J.; Lawrence, L.; Rummel, N.; & Aleven, V. (January 2022, Proceedings of the 23rd International Conference on Artificial Intelligence in Education, AIED 2023)

   Rodrigo, M.M.; Matsuda, N.; Cristea, A.I.; Dimitrova, V. (Ed.)

   It might be highly effective if students could transition dynamically between individual and collaborative learning activities, but how could teachers manage such complex classroom scenarios? Although recent work in AIED has focused on teacher tools, little is known about how to orchestrate dynamic transitions between individual and collaborative learning. We created a novel technology ecosystem that supports these dynamic transitions. The ecosystem integrates a novel teacher orchestration tool that provides monitoring support and pairing suggestions with two AI-based tutoring systems that support individual and collaborative learning, respectively. We tested the feasibility of this ecosystem in a classroom study with 5 teachers and 199 students over 22 class sessions. We found that the teachers were able to manage the dynamic transitions and valued them. The study contributes a new technology ecosystem for dynamically transitioning between individual and collaborative learning, plus insight into the orchestration functionality that makes these transitions feasible. 

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5. Tools in Their Toolbox: How Community College Faculty Transfer Industry Experience Into Their Teaching

   https://doi.org/10.1177/00915521211026677  

   Wagner, Brit; Zhu, Xiwei; Wang, Xueli (October 2021, Community College Review)

   Objective: This study is aimed at understanding the ways in which faculty at community colleges utilize their industry experiences to inform their teaching. Method: The research drew on Merriam’s basic qualitative approach in analyzing data from 14 semi-structured qualitative interviews. Results: Our findings expand upon prior research surrounding faculty development and community college faculty experiences. Our analysis revealed several themes in regard to how community college faculty with industry backgrounds follow diverse pathways leading to their teaching positions; how they teach using practical applications of concepts and sharing real-life examples; how they utilize their industry networks to enhance their academic programs and create practical opportunities for students; how they replicate workplace settings in their classrooms; and how they prepare students for their future careers. Contribution: Our study contributes new empirical evidence on the myriad ways in which faculty apply industry experiences in their instruction. The findings indicate that community colleges would benefit from offering targeted supports and pedagogical training for faculty with an industry background; encouraging faculty from all backgrounds and disciplines to share successful teaching strategies; and utilizing faculty members’ expertise, networks, and experiences from industry in mathematics-oriented classrooms. 

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