A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

Holstein, Kenneth; Aleven, Vincent; Rummel, Nikol

doi:10.1007/978-3-030-52237-7_20

Cited by 70 publications

(61 citation statements)

References 50 publications

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“…While there is no agreed definition of adaptivity in educational technology, previous frameworks characterise adaptivity at different levels of abstraction depending on their aim or the community where they emerge from (Holstein et al., 2020). Most definitions of adaptivity consider what adaptive systems respond to and when , how and why they adapt ie, they refer to the extent a digital system responds to learners' actions and/or cognitive or other psychological measures by providing appropriate modification or other forms of support (Aleven et al., 2017; EdSurge, 2016; Plass & Pawar, 2020; Scandura, 2014).…”

Section: Introductionmentioning

confidence: 99%

Designing for “challenge” in a large‐scale adaptive literacy game for primary school children

Benton

Mavrikis

Vasalou

et al. 2021

Brit J Educational Tech

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The use of learning games within the classroom is becoming increasingly common because of their potential to positively impact learning. Recent developments in adaptivity offer further possibilities to personalise learning by tailoring the game to an individual child's level or particular learning needs. However, designing an adaptive learning game is a complex process as many different game components have an impact on the provision of optimal challenge, crucial for maintaining player engagement, with limited prior work considering the multifaceted nature of this concept. This paper explores how to design for “challenge” within large‐scale adaptive learning games through a case study focused on the design of a literacy game for three linguistically and cognitively diverse learner groups—novice readers, children with dyslexia and children learning English as a foreign language. In reflecting on our design process, we identify three key design tensions that arose: (a) supporting longer‐term learning goals through game replayability; (b) fostering either replication or innovation in pedagogy through adaptivity rules; and (c) addressing diversity between learner groups. We present a set of design recommendations to guide researchers and designers in taking a multidimensional view of challenge when designing large‐scale adaptive learning games. What is already known about this topic? Adaptive learning games can have a positive impact on children's learning outcomes. Ensuring optimal challenge within games is important for maintaining engagement. Designing adaptive learning games is a complex process. What this paper adds? Designing for optimal challenge within adaptive learning game should be considered as a multifaceted concept. Identification of key tensions related to optimising challenge that can emerge during the design of large‐scale adaptive learning games. Recommendations for adaptivity researchers and learning game designers for how to address these tensions in adaptive learning game design. Implications for practice and/or policy? We need a more systematic approach to adaptivity game design to ensure wider spread adoption. Learning game designers seeking to utilise adaptive components in designing for optimal challenge should consider a focus on learners who may require a more targeted approach. Adaptive learning games offer opportunities for pedagogical innovation in the classroom through exploiting innovative game features as well as large‐scale data collection to support adaptive learning over time.

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Section: Introductionmentioning

confidence: 99%

Designing for “challenge” in a large‐scale adaptive literacy game for primary school children

Benton

Mavrikis

Vasalou

et al. 2021

Brit J Educational Tech

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show abstract

“…Co-orchestration describes a situation in which the responsibility for making orchestration decisions is shared across multiple entities and can be used to lower the orchestration load of teachers. Previous research has implicitly considered that roles can be shared among multiple entities (Sharples 2013;Prieto 2012;Prieto et al 2015) but only recently have researchers investigated what it means to have an effective shared control for different types of support (Holstein et al 2020). Much of the current research focuses on the design of co-orchestration systems to support teachers in supporting student learning during a single activity (Holstein et al 2019;van Leeuwen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Designing for the co-Orchestration of Social Transitions between Individual, Small-Group and Whole-Class Learning in the Classroom

Olsen

Rummel

Aleven

2020

Int J Artif Intell Educ

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Educational technologies are often developed such that students work on specific social levels (e.g., individual, small group, whole class) at specific times. However, in the reality of the classroom, learning activities are not so cleanly divided, with transitions occurring between social levels for students at different times. To support these social transitions in a way that can promote student learning, we need to lower the teacher’s orchestration load around managing fluid social transitions. Co-orchestration, in which the orchestration decisions are shared between different parties, can help to lower the orchestration load when it is designed according to the teacher’s values and classroom culture. In this paper, we present a taxonomy of social transitions and investigate how the responsibilities of orchestration can be divided between primary school teachers and a co-orchestration system in order to support the extension from rigid social transitions to fluid transitions in technology-enhanced classrooms. Across two studies, we used a design process involving co-design and prototyping with teachers. We uncovered and refined co-orchestration design desires that balance teachers’ orchestration loads while providing them with a sense of control. We present six design desires for maintaining a balance between teacher and system responsibilities regarding the orchestration of social transitions that can be implemented, such as in our mid-fidelity prototype, to support the range of social transitions. The list of desires contributes to co-orchestration research and more broadly technology design for classrooms by highlighting the changing balance of teacher control depending on what is the focus of the orchestration support.

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“…Educational human-AI interaction (see the path in Figure 6: human, education, artificial intelligence, technology; see nodes in Figure 7: human computer interaction, behavioral research). It is argued that human-AI interaction is a means to augment each other's abilities (e.g., Goal Augmentation, Perceptual Augmentation, Action Augmentation, and Decision Augmentation) [52]. Therefore, it is highlighted that AI "algorithms must be designed with awareness that they are part of a larger system consisting of humans" [53] (p. 33) and it is further noted that, as a result of rising machine agency [54], we need a clear set of guidelines and codes to regulate human-AI interaction [55] in an explicit and transparent manner [56].…”

Section: T-sne Text-mining and Social Network Analysismentioning

confidence: 99%

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

et al. 2021

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Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

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A Conceptual Framework for Human–AI Hybrid Adaptivity in Education

Cited by 70 publications

References 50 publications

Designing for “challenge” in a large‐scale adaptive literacy game for primary school children

Designing for “challenge” in a large‐scale adaptive literacy game for primary school children

Designing for the co-Orchestration of Social Transitions between Individual, Small-Group and Whole-Class Learning in the Classroom

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

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