Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we empirically show that different timesteps of DDIM inversion reveal varying subtle distinctions between synthetic and real images that are extractable for detection, taking the forms of such as Fourier power spectrum high-frequency discrepancies and inter-pixel variance distributions. Based on these observations, we propose a novel detection method named ESIDE that directly utilizes features of intermediately noised images by training an ensemble on multiple noised timesteps, circumventing the overtime of conventional reconstruction-based strategies. To enhance human comprehension, we introduce a metric-grounded explanation refinement module to identify and explain AI-generated flaws. Additionally, we present the benchmarks GenHard and GenExplain, offering detection samples of greater difficulty and high-quality rationales for fake images. Extensive experiments show that ESIDE achieves state-of-the-art performance with 98.91% and 95.89% detection accuracy on regular and challenging samples respectively, and demonstrates generalizability and robustness.

Feiran Zhang, Yixin Wu, Zhenghua Wang, Xiaohua Wang, Changze Lv, Xuanjing Huang, Xiaoqing Zheng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Game-Based Learning (GBL) is widely used to engage children through playful activities. However, it is unknown whether play elements or learning elements primarily drive children’s GBL performance. To bridge this gap, this paper presents a study involving 75 children aged 8-9 in GBL through two educational mathematical games. Using eye tracking, physiological sensing, motion capture, and gameplay logs, we captured children’s GBL experiences and examined how play and learning elements correlated with them. Our results demonstrated significant differences in children’s GBL experiences (as reflected in multimodal data) across play and learning elements. For example, we found learning elements, especially when combined with play elements, elicit higher engagement (as indicated by phasic electrodermal activity) but also increase cognitive load (as indicated by pupil dilation) in children. Whereas, play elements alone reduce cognitive load and broaden information processing. Furthermore, our study demonstrated that attention (as indicated by fixation duration), engagement, and information processing are positively associated with performance, whereas their cognitive load and stress (as indicated by heart-rate variability index) are negatively associated with performance.

Artificial Intelligence (AI) is playing a growing role in K-12 education.However, curricula often lack structure and proper assessment when paired with collaborative approaches like Design Thinking (DT).Here, behavioral and affective dynamics are overlooked, even though they are indicators of both performance and quality of the learning experience, warranting a more in-depth exploration through Multi-Modal Learning Analytics.Therefore, we engaged 63 students, divided into 29 groups (aged 11 to 15) in a DT workshop on AI, analyzing their performance across each stage of their experience, including their behavioral and affective (i.e., emotional) states, using data collected from physiological sensors, audio, and video recordings.Our results show that certain conditions (e.g., joint visual attention, boredom, and high stress) consistently predicted positive or negative performance across all stages of the workshop, while affective states such as confusion, frustration, high engagement, and low stress fluctuate with implications on the learning experience.

Design thinking (DT) is an approach used to address complex societal issues by engaging learners in hands-on problem-solving. Current educational guidelines seek to leverage the synergies between DT, digital literacy, and computational skills to enhance students' understanding and application in real-world matters, such as cyber-security and online awareness. However, tackling such learning experiences requires a shift in teaching approaches and the endorsement of educational technologies to foster digital agency. This empowers students to become proactive, informed participants in the digital future, starting from the classroom. To explore the feasibility of this approach from the students' perspectives, our study carried out a DT project with a block-based programming platform to emphasize the importance of cyber-security. Our study involved 113 students aged 10 to 12 across three K-12 schools. Of these, 52 agreed to participate in data collection via a semi-structured interview at the end of the project to share their experiences. We gained insights into students' perceptions regarding collaboration, technology interaction, and knowledge gained through thematic analysis of the interview data, considering both challenges and potential. For instance, we shed light on roadblocks arising from mismatched tool affordances and students' familiarity, as well as topic differentiation in elaboration based on participants' skills. At the same time, DT potential, when paired with digital tools, emerged to ease into everyday wicked problems in an engaging way and foster solution-oriented thinking.

Inquiry-based science learning is an educational strategy to enable students to actively engage in science learning concepts through inquiry activities such as experiments and observations. Gamification demonstrates a promising potential to engage children in learning contexts. In this regard, this paper presents Experiverse as an exemplar along with its associated six key design considerations to illustrate how to develop an application based on the concept of gamification and inquiry-based science learning for children. This paper reports on our experience evaluating Experiverse with 25 children (aged 9-13) in an informal setting based on data collected from log data, surveys and interviews to explore the feasibility of engaging children in science learning outside their classroom. Results indicated that children’s motivation (MO) significantly correlates with their enjoyment (PE) and perceived learning outcome (LOA) from using Experiverse. While children’s perceived learning outcome is significantly positively correlated with the number of view visits on Experiverse (EVV), the number of experiment view visits (EVV) is also significantly positively correlated with children’s perceived easiness of the app. Finally, this paper discusses the key findings of this study and points out the design implications for future research, like combining in-app experience and hands-on experimentation in real-life situations.

Design thinking (DT) and computational activities foster children’s knowledge capital for 21st-century literacies. The analysis of these activities often overlooks affective and behavioural states despite their significance in providing insights into children’s learning processes. Typically, these states and their changes are self-reported, lacking real-time capturing. Moreover, inquiries via Multi-Modal Data (MMD) for more comprehensive views are underrepresented in the current literature. We, therefore, conducted a DT activity focusing on coding engaging 33 children (aged 10 to 12) and analysed measurements including learning gain (from knowledge tests) and behavioural and affective states (from physiological sensors, video and voice recordings). Our results show that engagement and confusion exhibit positive correlations between MMD measurements and learning gain, while stress, frustration and anger stand out as detrimental for it. By mapping transitions in states experienced by the children, we unravelled negative learning scenarios that should be limited, along with positive indicators of increased performance.

Nowadays, learning activities have become more interactive and collaborative than ever before. However, it remains unclear what makes the group perform differently in such a learning context. With the empowerment of multimodal data (MMD), we conducted a field study involving 12 groups of children who collaborated during two-day-long classroom activities. This paper reports on a quantitative analysis and temporal explanation concerning the relation between children's performance and their group-level MMD measurements during a collaborative coding session in a design thinking activity. We computed each group's performance based on the created artefacts and compared the groups with better performance than the others. The results demonstrate that high-performing groups show more joint engagement, joint visual attention, and joint emotional intensity of delight, while low-performing groups show significantly more joint emotional intensity of frustration. In addition, the evolution over the four temporal phases showed different patterns between high and low-performing groups. Finally, this paper discusses design and theoretical implications for educators, researchers and practitioners.

Environmental education (EE) plays a vital role in engaging young people in exploring environmental issues and developing their sense of responsibility to the environment. Although gamification appears to be a promising way to motivate and engage K-12 students, it is unclear how it should be implemented in EE and whether it holds promising results similar to other contexts, such as science and engineering education. This paper reports a systematic literature review analysing the 28 papers published in the last five years. The results show how gamification has been employed to support EE and in what contexts. More specifically, the findings of this review contribute to our knowledge in the following three aspects: (1) EE strategies for implementing gamified interventions, (2) gamification strategies and elements utilised in EE, and (3) reported outcomes of gamified EE intervention. Finally, the paper discusses the implications for future related research on developing gamified interventions for EE.

Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

The increasing demand for medical services in hospitals has sparked interest in exploring alternative methods to support children with functional articulation disorders. Online speech games have emerged as a promising avenue to motivate children to engage in speech therapy. This study investigates the impact of gamification strategies on children's motivation within the context of speech rehabilitation games. Four distinct game prototypes were developed, with a specific emphasis on stimulating children's motivation to speak. Two sets of experiments involving 48 participants were conducted to assess the influence of (1) time-limitation and (2) interactive imitation objects on children's motivation. The results revealed that time limitation significantly increased motivation, while the effect of imitation objects on motivation was not statistically significant. These findings offer valuable insights into designing effective speech games for children. By leveraging gamification strategies in online speech games, we can address the motivation challenges faced by children with functional articulation disorders and potentially enhance the efficiency of speech therapy interventions.

There is growing interest in psychological interventions using socially assistive robots to mitigate distress and pain in the pediatric population. This work seeks to address the deficit in understanding of what features and functionality young children and their parents desire to help with pain management by using co-design, a common approach to exploring participants' imaginations and gathering design requirements. To close this gap, we carried out a co-design workshop involving seven families (with children aged between 4–6 and their parents) to understand their expectations and design preferences for a robot designed for pain management in children. Data were collected from surveys, video and audio recordings, interviews, and field notes. We present the robot prototypes constructed during the workshops and derive several preferences of the children (e.g, zoomorphic shape, distractors and emotional expressions as behaviors). Additionally, we report methodological insights regarding the involvement of young children and their parents in the co-design process. Based on the findings of this co-design study, we discuss personalization as a possible design concept for future child-robot interaction development.

This Research Full Paper presents a case study where university students (N=13) use a paper diary called EmoForm to self-track their emotions over a ten-week-long design-based learning course. Design-Based Learning (DBL) is a learning approach that enables students to learn through a sequence of design activities in a project-based learning or problem-based learning environment. Student's emotions are known to play an essential role in learning settings. We argue that students engaging in DBL will benefit from self-tracking of emotions. Such self-tracking will enable students to identify and control their emotions during the DBL process. The results of this study confirm that self-tracking with EmoForm enabled students' emotion awareness in DBL. In particular, our results illustrate how self-tracking with EmoForm impacted on students' DBL from three major aspects. We discuss design challenges regarding tools to support emotion awareness in DBL, and we present a summary of strategies conducive towards emotion awareness in DBL and the implications for future research.

Design-based learning (DBL) is attracting increasing attention for its potential to support informal learning, and as a way to enhance science and technology education at schools. However, related research has not yet considered the emotions children experience during DBL and how these affect the learning process. We report a case study aimed at developing a deeper understanding of children's emotional experience during DBL. In total 9 children (12-13 years old) are involved in this case study. In order to assess children's emotions during DBL lessons we used a self-reporting non-verbal instrument (the emotion card, which adapted from Five Degrees of Happiness Smiley Face Likert) and a verbal instrument (the Geneva Emotion Wheel Questionnaire, which contains 16 emotions). In addition, a group interview probed into the role of children's emotion during DBL. We discuss the methodological challenges exposed in this study, which will need to be addressed in future studies regarding the measurement of children's emotions in DBL.