Generative AI systems are trained on cultural content without consent from or consideration of heritage art communities who created it. In this paper, we examine how Generative AI intersects with traditional art through a case study of West Bengal’s Chitrakar painter-singer-storyteller community. Based on interviews and surveys with 10 Chitrakar artists, we find that significant AI literacy gaps exist within this heritage community, yet artists demonstrate informed resistance to AI integration based on awareness of ethical concerns and cultural appropriation. Our analysis shows that current AI models fail to authentically capture the cultural symbolism and technical depth of Chitrakar art. We contend that generic AI solutions are inadequate and hence propose community-centered interventions which ensure the continued vitality of heritage art.

Hypothesis formation and testing is central to debugging computer programs or algorithms, and thus considered an essential element of computational thinking (CT) education. To teach this skill to school children, we developed a two-player rule-guessing game called Block-N-Key which involves hypothesis generation and testing to categorise given blocks according to one of many possible rules. In an evaluation of the game with 10 children aged 8-12, participants were able to systematically generate and test hypotheses, indicating the potential usefulness and usability of the game in classroom settings. However, students needed guided instruction on: 1) the generation of complex hypotheses (e.g., using Boolean operations), 2) the use of falsification-not just confirmation-to test hypotheses, and 3) systematicity in keeping track of prior hypotheses and evidence to support limited working memory.

Generative Artificial Intelligence (GenAI) systems have experienced exponential growth in the last couple of years. These systems offer exciting capabilities for CS Education (CSEd), such as generating programs, that students can well utilize for their learning. Among the many dimensions that might affect the effective adoption of GenAI for CSEd, in this paper, we investigate students' trust. Trust in GenAI influences the extent to which students adopt GenAI, in turn affecting their learning. In this paper, we present results from a survey of 253 students at two large universities to understand how much they trust GenAI tools and their feedback on how GenAI impacts their performance in CS courses. Our results show that students have different levels of trust in GenAI. We also observe different levels of confidence and motivation, highlighting the need for further understanding of factors impacting trust.

No abstract available.

Recent enhancements to the spreadsheet formula language and intelligent spreadsheet interfaces allow spreadsheet users to build more complex spreadsheets in systematic ways (e.g., via functional abstractions). However, users have been slow to adopt such features, partly due to the absence of corresponding improvements in tools such as editors and debuggers. In this paper, we present FxD, a novel spreadsheet debugging interface, which provides structured information needed for spreadsheet users to debug formulas in systematic ways through affordances such as the ability to step into the execution of dependencies and provide contextual information to users based on the current context. An in-vitro, within-subject (n=12) experiment revealed that, even though using FxD did not lead to faster debugging, participants reported qualitative improvements (e.g., feelings of efficiency and capability) when debugging with it. Further, participants were more satisfied with the amount of information provided by FxD and felt that it would enhance their existing debugging workflows. Our results have implications for the design of debuggers for spreadsheets and for functional programming languages in general.

End-user programming (EUP) empowers regular computer users to build and customize software on their own, but we suspect that most developments in the field build on the preferences of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) users. This potentially leaves behind 85 % of the world's population that lives in non-WEIRD societies. To validate whether this suspicion has merit, we conducted a systematic mapping study of the research papers (N=217) published on EUP since 2019. Our results indicate that EUP research is heavily WEIRD-centric. First, over 80% of papers in our final analysis (N=159) had WEIRD-only authorship. Second, 51-94% of papers reporting user studies (N=86) recruited WEIRD participants. Finally, our results suggest that research in EUP is largely driven by the advancements in Industrialized societies. We consider the implications of this WEIRD-ness, and discuss avenues through which to serve more diverse end-user programmcrs.

End-user programming (EUP) gives ordinary computer users the ability to create and modify software on their own. However, we suspect that most developments in EUP are based on the preferences of WEIRD (Western, Educated, Industrialised, Rich, and Democratic) populations. Only 15% of the world's population live in WEIRD nations and therefore any WEIRD bias in EUP research and tools potentially excludes the vast majority of the people from reaping its benefits. Therefore, to investigate the existence and the nature of any WEIRDness bias in EUP, we conducted a systematic mapping study of the research papers (N=217) published on EUP since 2019. Our results suggest that EUP research is WEIRD in three ways, namely researcher diversity, research participant diversity, and the application domains the research caters to. We discuss the implications of these findings and suggest ways to improve the current situation.

Writing formulas on the spreadsheet grid is arguably the most widely practiced form of programming. Still, studies highlight the difficulties experienced by end-user programmers when learning and using traditional formulas, especially for slightly complex tasks. The purpose of GridBook is to ease these difficulties by supporting formulas expressed in natural language within the grid; it is the first system to do so.

The value of computational abstractions to non-expert end-user programmers is contentious. We study reactions to the lambda function in Microsoft Excel, which enables users to define their own functions using the spreadsheet formula language, through a thematic analysis of nearly 2,700 comments posted on the Reddit, Hacker News, YouTube, and Microsoft Tech Community online forums. We find that computational abstractions are viewed both as helpful and harmful, that users encounter learning and understanding barriers to applying them, and that there are deficiencies and opportunities in tooling such as in formula editing, versioning, reuse and sharing. We find that the introduction of lambda prompts new debate around whether spreadsheets are code, whether writing formulas can be considered programming, and whether spreadsheet users identify themselves as programmers.

We operationalized information foraging theory for Stack Overflow and built a semi-supervised model to recommend optimal information to the developers.

End-user programmers opportunistically copy-and-paste code snippets from colleagues or the web to accomplish their tasks. Unfortunately, these snippets often don’t work verbatim, so these people—who are non-specialists in the programming language—make guesses and tweak the code to understand and apply it successfully. To support their desired workflow and facilitate tweaking and understanding, we built a prototype tool, TweakIt, that provides users with a familiar live interaction to help them understand, introspect, and reify how different code snippets would transform their data. Through a usability study with 14 data analysts, participants found the tool to be useful to understand the function of otherwise unfamiliar code, to increase their confidence about what the code does, to identify relevant parts of code specific to their task, and to proactively explore and evaluate code. Overall, our participants were enthusiastic about incorporating TweakIt in their own day-to-day work.

Spreadsheet users routinely read, and misread, others' spreadsheets, but literature offers only a high-level understanding of users’ comprehension behaviors. This limits our ability to support millions of users in spreadsheet comprehension activities. Therefore, we conducted a think-aloud study of 15 spreadsheet users who read others’ spreadsheets as part of their work. With qualitative coding of participants’ comprehension needs, strategies and difficulties at 20-second granularity, our study provides the most detailed understanding of spreadsheet comprehension to date.

Version Control Systems (VCS) are an important source of information for developers. This calls for a principled understanding of developers' information seeking in VCS-both for improving existing tools and for understanding requirements for new tools. Our prior work investigated empirically how and why developers seek information in VCS: in this paper, we complement and enrich our prior findings by reanalyzing the data via a theory's lens. Using the lens of Information Foraging Theory (IFT), we present new insights not revealed by the prior empirical work. First, while looking for specific information, participants' foraging behaviors were consistent with other foraging situations in SE; therefore, prior research on IFT-based SE tool design can be leveraged for VCS. Second, in change awareness foraging, participants consumed similar diets, but in subtly different ways than in other situations; this calls for further investigations into change awareness foraging. Third, while committing changes, participants attempted to enable future foragers, but the competing needs of different foraging situations led to tensions that participants failed to balance: this opens up a new avenue for research at the intersection of IFT and SE, namely, creating forageable information. Finally, the results of using an IFT lens on these data provides some evidence as to IFT's scoping and utility for the version control domain.

Foraging among similar variants of the same artifact is a common activity, but computational models of Information Foraging Theory (IFT) have not been developed to take such variants into account. Without being able to computationally predict people's foraging behavior with variants, our ability to harness the theory in practical ways--such as building and systematically assessing tools for people who forage different variants of an artifact--is limited. Therefore, in this paper, we introduce a new predictive model, PFIS-V, that builds upon PFIS3, the most recent of the PFIS family of modeling IFT in programming situations. Our empirical results show that PFIS-V is up to 25% more accurate than PFIS3 in predicting where a forager will navigate in a variationed information space.

Foraging among too many variants of the same artifact can be problematic when many of these variants are similar. This situation, which is largely overlooked in the literature, is commonplace in several types of creative tasks, one of which is exploratory programming. In this paper, we investigate how novice programmers forage through similar variants. Based on our results, we propose a refinement to Information Foraging Theory (IFT) to include constructs about variation foraging behavior, and propose refinements to computational models of IFT to better account for foraging among variants.

Despite software history being indispensable for developers, there is little empirical knowledge about how they examine software history. Without such knowledge, researchers and tool builders are in danger of making wrong assumptions and building inadequate tools. In this paper we present an in-depth empirical study about the motivations developers have for examining software history, the strategies they use, and the challenges they encounter. To learn these, we interviewed 14 experienced developers from industry, and then extended our findings by surveying 217 developers. We found that history does not begin with the latest commit but with uncommitted changes. Moreover, we found that developers had different motivations for examining recent and old history. Based on these findings we propose 3-LENS HISTORY, a novel unified model for reasoning about software history.