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The rapid advancement of Generative AI (Gen-AI) is transforming Human-Computer Interaction (HCI), with significant implications across various sectors. This study investigates the public's perception of Sora OpenAI, a pioneering Gen-AI video generation tool, via social media discussions on Reddit before its release. It centers on two main questions: the envisioned applications and the concerns related to Sora's integration. The analysis forecasts positive shifts in content creation, predicting that Sora will democratize video marketing and innovate game development by making video production more accessible and economical. Conversely, there are concerns about deepfakes and the potential for disinformation, underscoring the need for strategies to address disinformation and bias. This paper contributes to the Gen-AI discourse by fostering discussion on current and future capabilities, enriching the understanding of public expectations, and establishing a temporal benchmark for user anticipation. This research underscores the necessity for informed, ethical approaches to AI development and integration, ensuring that technological advancements align with societal values and user needs.

Students’ increasing use of Artificial Intelligence (AI) presents new challenges for assessing their mastery of knowledge and skills in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students’ AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students’ use of AI in PBL and ways of analyzing such usage grounded by students’ vision of how educational goals may transform. We also found that students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand their use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.

The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students' use of AI in PBL and ways of analyzing these uses grounded by students' vision of education goal transformation. We also found students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand the use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning., Comment: Conditionally accepted by CHI '24

This workshop paper presents a critical examination of the integration of Generative AI (Gen AI) into the academic writing process, focusing on the use of AI as a collaborative tool. It contrasts the performance and interaction of two AI models, Gemini and ChatGPT, through a collaborative inquiry approach where researchers engage in facilitated sessions to design prompts that elicit specific AI responses for crafting research outlines. This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work. Preliminary findings suggest that prompt variation significantly affects output quality and reveals distinct capabilities and constraints of each model. The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models, ultimately aiming to enhance AI-assisted academic writing and prompt a deeper dialogue within the HCI community., Comment: 5 pages, workshop paper, CHI 2024 conference GENAI

In recent years, the gamification research community has widely and frequently questioned the effectiveness of one-size-fits-all gamification schemes. In consequence, personalization seems to be an important part of any successful gamification design. Personalization can be improved by understanding user behavior and Hexad player/user type. This paper comes with an original research idea: It investigates whether users' game-related data (collected via various gamer-archetype surveys) can be used to predict their behavioral characteristics and Hexad user types in non-game (but gamified) contexts. The affinity that exists between the concepts of gamification and gaming provided us with the impetus for running this exploratory research. We conducted an initial survey study with 67 Stack Exchange users (as a case study). We discovered that users' gameplay information could reveal valuable and helpful information about their behavioral characteristics and Hexad user types in a non-gaming (but gamified) environment. The results of testing three gamer archetypes (i.e., Bartle, Big Five, and BrainHex) show that they can all help predict users' most dominant Stack Exchange behavioral characteristics and Hexad user type better than a random labeler's baseline. That said, of all the gamer archetypes analyzed in this paper, BrainHex performs the best. In the end, we introduce a research agenda for future work., Comment: This manuscript is the pre-print version of our paper accepted at the conference of HCI International 2023

We share the largest dataset for the Pakistani Twittersphere consisting of over 49 million tweets, collected during one of the most politically active periods in the country. We collect the data after the deposition of the government by a No Confidence Vote in April 2022. This large-scale dataset can be used for several downstream tasks such as political bias, bots detection, trolling behavior, (dis)misinformation, and censorship related to Pakistani Twitter users. In addition, this dataset provides a large collection of tweets in Urdu and Roman Urdu that can be used for optimizing language processing tasks.

The application of AI-powered tools has piqued the interest of many fields, particularly in the academic community. This study uses ChatGPT, currently the most powerful and popular AI tool, as a representative example to analyze how the Chinese public perceives the potential of large language models (LLMs) for educational and general purposes. Although facing accessibility challenges, we found that the number of discussions on ChatGPT per month is 16 times that of Ernie Bot developed by Baidu, the most popular alternative product to ChatGPT in the mainland, making ChatGPT a more suitable subject for our analysis. The study also serves as the first effort to investigate the changes in public opinion as AI technologies become more advanced and intelligent. The analysis reveals that, upon first encounters with advanced AI that was not yet highly capable, some social media users believed that AI advancements would benefit education and society, while others feared that advanced AI, like ChatGPT, would make humans feel inferior and lead to problems such as cheating and a decline in moral principles. The majority of users remained neutral. Interestingly, with the rapid development and improvement of AI capabilities, public attitudes have tended to shift in a positive direction. We present a thorough analysis of the trending shift and a roadmap to ensure the ethical application of ChatGPT-like models in education and beyond.

The emergence of the metaverse is being widely viewed as a revolutionary technology owing to a myriad of factors, particularly the potential to increase the accessibility of learning for students with disabilities. However, not much is yet known about the views and expectations of disabled students in this regard. The fact that the metaverse is still in its nascent stage exemplifies the need for such timely discourse. To bridge this important gap, we conducted a series of semi-structured interviews with 56 university students with disabilities in the United States and Hong Kong to understand their views and expectations concerning the future of metaverse-driven education. We have distilled student expectations into five thematic categories, referred to as the REEPS framework: Recognition, Empowerment, Engagement, Privacy, and Safety. Additionally, we have summarized the main design considerations in eight concise points. This paper is aimed at helping technology developers and policymakers plan ahead of time and improving the experiences of students with disabilities., Comment: This paper has been accepted for presentation at the L@S 2023 conference. The version provided here is the pre-print manuscript

To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user experience (UX) and views of early adopters of ChatGPT across different educational sectors. The results of our research show that ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. In social media dialogues, the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics. Early adopters' attitudes towards ChatGPT are multifaceted. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users. They worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students' social and critical thinking skills. This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts. Our investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies., Comment: This is the authors' preprint version of the paper accepted by the Journal of Computers in Human Behavior: Artificial Humans (doi: https://doi.org/10.1016/j.chbah.2023.100027)

With the advancement of virtual reality (VR) technology, virtual displays have become integral to how museums, galleries, and other tourist destinations present their collections to the public. However, the current lack of immersion in virtual reality displays limits the user's ability to experience and appreciate its aesthetics. This paper presents a case study of a creative approach taken by a tourist attraction venue in developing a physical network system that allows visitors to enhance VR's aesthetic aspects based on environmental parameters gathered by external sensors. Our system was collaboratively developed through interviews and sessions with twelve stakeholder groups interested in art and exhibitions. This paper demonstrates how our technological advancements in interaction, immersion, and visual attractiveness surpass those of earlier virtual display generations. Through multimodal interaction, we aim to encourage innovation on the Web and create more visually appealing and engaging virtual displays. It is hoped that the greater online art community will gain fresh insight into how people interact with virtual worlds as a result of this work., Comment: Accepted In Proceedings of the ACM Web Conference 2023, April 30-May 4, 2023, Austin, TX, USA. ACM, New York, NY, USA

There has been a significant expansion in the use of online social networks (OSNs) to support people experiencing mental health issues. This paper studies the role of Instagram influencers who specialize in coaching people with mental health issues. Using a dataset of 97k posts, we characterize such users' linguistic and behavioural features. We explore how these observations impact audience engagement (as measured by likes). We show that the support provided by these accounts varies based on their self-declared professional identities. For instance, Instagram accounts that declare themselves as Authors offer less support than accounts that label themselves as Coach. We show that increasing information support in general communication positively affects user engagement. However, the effect of vocabulary on engagement is not consistent across the Instagram account types. Our findings shed light on this understudied topic and guide how mental health practitioners can improve outreach.

More and more learning apps like Duolingo are using some form of gamification (e.g., badges, points, and leaderboards) to enhance user learning. However, they are not always successful. Gamification misuse is a phenomenon that occurs when users become too fixated on gamification and get distracted from learning. This undesirable phenomenon wastes users' precious time and negatively impacts their learning performance. However, there has been little research in the literature to understand gamification misuse and inform future gamification designs. Therefore, this paper aims to fill this knowledge gap by conducting the first extensive qualitative research on gamification misuse in a popular learning app called Duolingo. Duolingo is currently the world's most downloaded learning app used to learn languages. This study consists of two phases: (I) a content analysis of data from Duolingo forums (from the past nine years) and (II) semi-structured interviews with 15 international Duolingo users. Our research contributes to the Human-Computer Interaction (HCI) and Learning at Scale (L@S) research communities in three ways: (1) elaborating the ramifications of gamification misuse on user learning, well-being, and ethics, (2) identifying the most common reasons for gamification misuse (e.g., competitiveness, overindulgence in playfulness, and herding), and (3) providing designers with practical suggestions to prevent (or mitigate) the occurrence of gamification misuse in their future designs of gamified learning apps., Comment: Accepted in the Proceedings of the 9th ACM Conference on Learning @ Scale (L@S '22)

As more and more face-to-face classes move to online environments, it becomes increasingly important to explore any emerging barriers to students' learning. This work focuses on characterizing student barriers to active learning in synchronous online environments. The aim is to help novice educators develop a better understanding of those barriers and prepare more student-centered course plans for their active online classes. Towards this end, we adopt a qualitative research approach and study information from different sources: social media content, interviews, and surveys from students and expert educators. Through a thematic analysis, we craft a nuanced list of students' online active learning barriers within the themes of human-side, technological, and environmental barriers. Each barrier is explored from the three aspects of frequency, importance, and exclusiveness to active online classes. Finally, we conduct a summative study with 12 novice educators and explain the benefits of using our barrier list for course planning in active online classes., Comment: Accepted in the Proceedings of the Eighth ACM Conference on Learning @ Scale (L@S '21)

Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems. Question Pool websites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL by supplying authentic, diverse, and contextualized questions to students. Nonetheless, empirical findings suggest that 40% to 80% of students registered in QPs drop out in less than two months. This research is the first attempt to understand and predict student dropouts from QPs via exploiting students' engagement moods. Adopting a data-driven approach, we identify five different engagement moods for QP students, which are namely challenge-seeker, subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find that students have collective preferences for answering questions in each engagement mood, and deviation from those preferences increases their probability of dropping out significantly. Last but not least, this paper contributes by introducing a new hybrid machine learning model (we call Dropout-Plus) for predicting student dropouts in QPs. The test results on a popular QP in China, with nearly 10K students, show that Dropout-Plus can exceed the rival algorithms' dropout prediction performance in terms of accuracy, F1-measure, and AUC. We wrap up our work by giving some design suggestions to QP managers and online learning professionals to reduce their student dropouts., Comment: Accepted in the 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2021)

This research has been carried out in the Experimental Garden of the Faculty of Agriculture, Riau Islamic University, Jalan Kaharudin Nasution Km 11, Kelurahan Air Dingin, Bukit Raya District, Pekanbaru City. The research was carried out for 4.5 months from April to August 2019. The purpose of this study was to determine the effect of interaction and the main effect of trichocompost and NPK 16:16:16 pearl on the production of luffa plants (Luffa acutangula L.). The design used is a completely randomized design (CRD) factorial consisting of two factors. The first factor was trichocompost consisting of 4 treatment levels, namely 0; 0.9; 1.8 and 2.7 kg / plot while the second factor is NPK Mutiara 16:16:16 which consists of 4 levels, 0; 10; 20 and 30 g / plant and 16 treatment combinations consisted of 3 replications, so that 48 experimental plots were obtained. Where each unit plot consists of 4 plants, 2 plants as samples, to obtain a total of 192 plants. From the results of the research concluded the following: The interaction of giving trichocompost fertilizer and NPK Mutiara 16:16:16 fertilizer is real to the percentage of flowers to fruit, fruit weight per plot, fruit weight per plant, fruit weight per fruit and fruit length, with treatment best trichocompost fertilizer 2.7 kg / plot and NPK Mutiara 16:16:16 fertilizer dose 30 g / plant. The main effect of trichocompost fertilizer was evident on all observed parameters, with the best treatment of 2.7 kg / plot trichocompost fertilizer. The main effect of NPK Mutiara 16:16:16 fertilizer is real on all parameters of observation, with the best treatment NPK Mutiara 16:16:16 dose 30 g / plant.