Children’s lives are increasingly intertwined with AI systems, from recommender algorithms to generative models, raising concerns about potential impacts on children’s agency. Although supporting human agency, autonomy, and empowerment is a widely shared HCI goal, we lack clear definitions of these concepts in designing child-AI interaction. Through a review of 25 recent HCI studies, we find agency is rarely explicitly defined and its conceptualisation varies across something children innately possess and something to be developed. Our literature mapping shows that researchers observed agency through children’s planning and self-regulation, asserting control over AI systems, and critique and re-design of the status quo. Conditions reported by researchers that enable or constrain agency span epistemic conditions, interactional design, social context, and motivational orientation. Our review highlights gaps in research on designing for children’s agency. We advocate for conceptual clarity by drawing upon existing frameworks and highlight the importance of considering children’s agency through a relational lens.

Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.

Outcome and elaborative feedback in games can scaffold learners to recognise errors and apply corrective strategies. However, there is little evidence that indicates how children process such feedback. Using an active intervention approach, this study empirically evaluated how three groups of primary-aged children with different profiles-novice readers, children with reading difficulties, and children learning English as a foreign language-attended to, understood, and acted upon feedback within a digital literacy game. Children's gameplay and verbalisations across groups were compared through systematic video analysis. Our findings demonstrate that all readers benefited from visual, non-verbal outcome feedback, which supported accurate interpretations of their performance, but groups attended to it differentially. Older children noticed auditory, verbal elaborative feedback more than novice readers, but all children struggled to understand it, instead relying on implicit knowledge to correct future responses. We conclude by highlighting several contributions to games-based learning research, game design, and pedagogical practice.

Despite the recognition of the need to include practitioners in the design of learning analytics (LA), especially teacher input tends to come later in the design process rather than in the definition of the initial design agenda. This paper presents a case study of a design project tasked with developing LA tools for a reading game for primary school children. Taking a co-design approach, we use the Inspiration Cards Workshop to promote meaningful teacher involvement even for participants with low background in data literacy or experience in using learning analytics. We discuss opportunities and limitations of using the Inspiration Cards Workshops methodology, and particularly Inspiration Cards as a design tool, to inform future LA design efforts.

This paper reflects on a user-centered design methodology for requirements elicitation at early stages of a \ndesign process for Learning Analytics tools. This methodology may be used as a domain specific instrument \nto elicit user perspectives about the communicational aspects of learning analytics dashboards. The focus of \nthis work is identifying ways to communicate the data analysis findings in a way that is easily perceptible \nand facilitates actionable decision making. We present the structure as well as the logic behind the design of \nthis instrument. As a case study, the paper describes an implementation of this methodology in the context of \nschool-wide analytics communicating to stakeholders quality indicators through summarising and visualising \ndata collected through student and parent surveys. We provide high-level and transferable recommendations \nderived from the analysis of the workshop with key stakeholders and identify future improvements in our \nmethodology.

Learning games now play a role in both formal and informal learning, including foundational skills such as literacy. While feedback is recognised as a key pedagogical dimension of these games, particularly in early learning, there has been no research on how commercial games available to schools and parents reify learning theory into feedback. Using a systematic content analysis, we examine how evidence-based feedback principles manifest in five widely-used learning games designed to foster young children's reading skills. Our findings highlight strengths in how games deliver feedback when players succeed. Many of the games, however, were inconsistent and not proactive when providing error feedback, often promoting trial and error strategies. Furthermore, there was a lack of support for learning the game mechanics and a preference for task-oriented rewards less deeply embedded in the gameplay. Our research provides a design and research agenda for the inclusion of feedback in early learning games.

This paper presents a tool for the configuration of logging and authoring of automated feedback for exploratory learning objects (ELOs). This tool has been developed in the context of a larger project that is developing a platform for authoring interactive educational e-books. This platform comprises an extendable set of diverse widgets that can be used to generate instances of exploratory activities that can be employed in various learning scenarios. AuthELO was designed and developed to provide a simple, common and efficient authoring interface that can normalise the heterogeneity of these widgets and give the ability to non-experts to easily modify — if not program themselves — the feedback that is provided to students based on their interaction. We describe the architecture and design characteristics of AuthELO and present a small-scale evaluation of the prototype that shows promising results.

Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.

This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.

This paper presents an analytics dashboard that has been developed for designers of interactive e-books. This is part of the EU-funded MC Squared project that is developing a platform for authoring interactive educational e-books. The primary objective is to develop technologies and resources that enhance creative thinking for both designers (authors) and learners. The learning material is expected to offer learners opportunities to engage creatively with mathematical problems and develop creative mathematical thinking. The analytics dashboard is designed to increase authors' awareness so that they can make informed decisions on how to redesign and improve the e-books. This paper presents architectural and design decisions on key features of the dashboard and discusses future steps with respect to the potential for exploratory data analysis.

We present our approach to designing and evaluating tools that can assist teachers in classroom settings where students are using Exploratory Learning Environments (ELEs), using as our case study the MiGen system, which targets 1114 year old students' learning of algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the whole class and inform their interventions. We present the design and evaluation approach followed during the development of MiGen's Teacher Assistance tools, drawing parallels with the recently proposed LATUX workflow but also discussing how we go beyond this to include a large number of teacher participants in our evaluation activities, so as to gain the benefit of different view points. We discuss the results of the evaluations, which show that participants appreciated the capabilities of the tools and were mostly able to use them quickly and accurately.

We study how the use of online learning systems stimulate cognitive activities, by conducting an experiment with the use of eye tracking technology to monitor eye fixations of 60 final year students engaging in online interactive tutorials at the start of their Final Year Project module. Our findings show that the students' visual scanning behaviours fall into three different types of eye fixation patterns, and the data corresponding to the different types relates to the performance of the students in other related academic modules. We conclude that this method of studying eye fixation patterns can identify different types of learners with respect to cognitive activities and academic potentials, allowing educators to understand how their instructional design using online learning environments can stimulate higher-order cognitive activities.

This paper presents the initial results of a study where we embrace and test Entwistle’s theory in order to explain the students’ interaction when using interactive learning environments (ILEs) in real conditions. In contrast to others who seek ways of adapting a system to students’ specific learning style, our aim is to find ways of designing an ILE that encourages students’ adoption of a deep approach to studying and discourages a surface approach. In this paper we focus only on the relationship between two subscales of the Approaches and Study Skills Inventory for Students (the deep subscale of interest in ideas and surface subscale of fear of failure), and metrics occurring from usage in a specific ILE. The results point to insights for instructors regarding the integration of ILEs in classrooms, as well as suggestions regarding the design of features which encourage a deep approach to studying.

Copyright © 2015 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. This paper provides a knowledge representation process for authoring of learning experiences that capture feedback designed in the context of learning environments. The paper reports on a year long study with designers who are creating mathematical teaching and learning resources as part of an EU project (M C Squared). In this paper we examine the knowledge representation process we used in design and creation of a mediation vocabulary. The model to be designed has to provide different layers of 'knowledge integration' and thus offers insights into the importance of knowledge mediation in the emergence of new learning environments and experiences. Hence, authoring of designs and feedback through use of ontologies to form part of the annotating of the learning activities. The annotations form part of the context to be used as part of the learning analytics.

This paper presents a technique that enables integration and interoperability of web components with learning platforms. This technique is proposed as a lightweight alternative to IMS LTI and OpenAjax and is especially suited to simple client-side widgets that have no back-end dependencies and potential security risks. The technique has already been used successfully in an experimental learning platform to provide data generated by various heterogeneous components for intelligent support and learning analytics.

Affective states play a significant role in students' learning behaviour. Positive affective states can enhance learning, whilst negative affective states can inhibit it. This paper describes a Wizard-of-Oz study which investigates whether the way feedback is presented should change according to the affective state of a student, in order to encourage affect change if that state is negative. We presented 'high-interruptive' feedback in the form of pop-up windows in which messages were immediately viewable; or 'low-interruptive' feedback, a glowing light bulb which students needed to click in order to access the messages. Our results show that when students are confused or frustrated high-interruptive feedback is more effective, but when students are enjoying their activity, there is no difference. Based on the results, we present guidelines for adaptively tailoring the presentation of feedback based on students' affective states when interacting with learning environments.

Book synopsis: Brand New Book. This publication covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED). AIED2009 is part of an ongoing series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. The conference provides opportunities for the cross-fertilization of techniques from many fields that make up this interdisciplinary research area, including: artificial intelligence, computer science, cognitive and learning sciences, education, educational technology, psychology, philosophy, sociology, anthropology, linguistics and the many domain-specific areas for which AIED systems have been designed and evaluated. AIED2009 focuses on the theme 'Building learning systems that care: from knowledge representation to affective modelling'. The key research question is how to tackle the complex issues related to building learning systems that care, ranging from representing knowledge and context to modelling social, cognitive, metacognitive and affective dimensions.This requires multidisciplinary research that links theory and technology from artificial intelligence, cognitive science and computer science with theory and practice from education and the social sciences.

Book synopsis: This publication covers papers presented at the Artificial Intelligence in Education conference 2009 (AIED). AIED2009 is part of an ongoing series of biennial international conferences for top quality research in intelligent systems and cognitive science for educational computing applications. The conference provides opportunities for the cross-fertilization of techniques from many fields that make up this interdisciplinary research area, including: artificial intelligence, computer science, cognitive and learning sciences, education, educational technology, psychology, philosophy, sociology, anthropology, linguistics and the many domain-specific areas for which AIED systems have been designed and evaluated. AIED2009 focuses on the theme 'Building learning systems that care: from knowledge representation to affective modelling'. The key research question is how to tackle the complex issues related to building learning systems that care, ranging from representing knowledge and context to modelling social, cognitive, metacognitive and affective dimensions.This requires multidisciplinary research that links theory and technology from artificial intelligence, cognitive science and computer science with theory and practice from education and the social sciences.

Although the notion of generality is central in mathematics and science, beingable to identify and express general patterns and/or articulate structures is one of the main difficulties for children when they learn mathematics. This paper presents a step towards a set of tools that addresses this problem by encouraging students to connect between actual activities within the task and expressions of generality. The paper introduces a novel tool -- ShapeBuilder -- and describes how it can be used in the context of a well-known generalisation activity: tiling a pond. Insights gained from various small-scale exploratory studies with children are discussed followed by a description of how we expect this tool to develop in future work.

We describe how a user-centred engineering cycle is employed during the development of a Web-based instructional system for first year science and engineering students learning mathematics. After an overview of the relevant methodology and appropriate theory that influences the system's design, we briefly present the system together with the steps that led to its integration in the teaching environment as well as to reusable materials and components for other environments.