Designing a conversational agent for supporting data exploration in citizen science

Stein, Carolin; Teubner, Timm; Morana, Stefan Article — Published Version Designing a conversational agent for supporting data exploration in citizen science Electronic Markets Provided in Cooperation with: Springer Nature Suggested Citation: Stein, Carolin; Teubner, Timm; Morana, Stefan (2024) : Designing a conversational agent for supporting data exploration in citizen science, Electronic Markets, ISSN 1422-8890, Springer, Berlin, Heidelberg, Vol. 34, Iss. 1, https://doi.org/10.1007/s12525-024-00705-3 This Version is available at: https://hdl.handle.net/10419/315791 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Electronic Markets (2024) 34:23 https://doi.org/10.1007/s12525-024-00705-3 RESEARCH PAPER Designing aconversational agent forsupporting data exploration incitizen science CarolinStein1,2 · TimmTeubner3,4· StefanMorana5 Received: 31 July 2023 / Accepted: 14 February 2024 / Published online: 27 March 2024 © The Author(s) 2024 Abstract Data is ubiquitous in today’s digitized society. However, access to and literacy in handling data plays a pivotal role in determining who can benefit from it and who can use—or potentially misuse—it. To combat inequalities and address issues such as misinformation, it is essential to enable citizens to effectively access and understand data within their local ecosystems. To address this challenge, we focus on the case of citizen science and propose using a conversational agent to support data exploration and lower barriers to citizen engagement in research projects. Using a design science research approach, we derive design principles and develop a prototypical artifact. Moreover, we conduct an experimental evaluation, demonstrating strong interest among citizens to participate in scientific data analysis and that conversational agents hold great potential in increasing data literacy. Keywords Conversational agents· Citizen science· Design science research JEL Classification O35· O36· I24 Introduction The emergence of the digital era has undeniably amplified the profound impact of data on all aspects of our lives. Technological advancements have enabled the collection and storage of large datasets (Clarke, 2016; Twidale etal., 2013). Data-driven business models motivate companies to collect and analyze increasing amounts of data, in some cases even at the expense of their customers’ interests (Trzaskowski, 2022). While the increased availability of data can lead to new insights and better decisions, it also accentuates issues of inequality and exploitation (D’Ignazio, 2022). Ownership and literacy of data are critical factors in determining who can effectively utilize data to their advantage (D’Ignazio, 2022). This becomes particularly concerning as data and its products can be misused for personal, political, or economic reasons (Carmi etal., 2020; Pullinger, 2021; Trzaskowski, 2022). The evolving landscape of generative AI poses further challenges with the proliferation of disinformation in the digital realm (Hanley & Durumeric, 2023). In this context, data literacy becomes essential, not just for actively engaging in public debates and decision-making (Debruyne etal., 2021; Radermacher, 2021; Schüller etal., 2019) but also for navigating the digital landscape in general (Carmi etal., 2020). Despite its evident significance, a substantial portion of the population still lacks adequate data literacy, relegating them to passive “data subjects” rather than empowered data users (D’Ignazio, 2022). This data literacy divide perpetuates inequalities and denies individuals the Responsible Editor: Alfred Zimmermann * Carolin Stein [email protected] Timm Teubner [email protected] Stefan Morana stef[email protected] 1 FZI Research Center forInformation Technology, Haid-Und-Neu-Straße 10-14, 76131Karlsruhe, Germany 2 KIT Karlsruhe Institute ofTechnology, Kaiserstraße 89-93, 76133Karlsruhe, Germany 3 TUB Technical University ofBerlin, Straße des 17. Juni 135, 10623Berlin, Germany 4 Einstein Center Digital Future, Wilhelmstraße 67, 10117Berlin, Germany 5 Saarland University, Campus C3 1, 66123Saarbrücken, Germany Electronic Markets (2024) 34:2323 Page 2 of 18 agency to benefit from the data-driven landscape (D’Ignazio, 2022). Consequently, the quest for effective countermeasures becomes a critical pursuit. A promising use case in this regard is citizen science (CS) (Twidale etal., 2013), referring to the participation of non-professionals in scientific research activities (Shirk etal., 2012). Historically, CS strived to democratize science and counteract social inequity (Irwin, 1995). Although participatory activities vary, in many CS projects, participants can access and work with scientific data (National Academies of Sciences & Medicine, 2018). It is hence a natural fit when thinking about conveying data literacy and shifting modes of power and agency. Currently, however, several barriers prevent the full realization of educational benefits. First, while many CS projects enable participation in data collection, participation in consecutive exploration and interpretation of data is sparse (Monzón Alvarado etal., 2020). The complexity or confidentiality of data and tasks can reduce the offer of activities (Kloetzer etal., 2021). Second, researchers and project initiators are limited in resources, such as funding and time (Kloetzer etal., 2021; Wald etal., 2016) necessary to organize participation and support. This makes current educational CS tools such as (peer) mentoring, tutorials and trainings, or curriculums potentially unsuitable as they imply additional efforts for researchers or the community (National Academies of Sciences & Medicine, 2018). While advisory bodies call for data literacy to be actively addressed in CS projects (National Academies of Sciences & Medicine, 2018), given the current challenges, appropriate solutions must first be explored. Specifically, to succeed on a larger scale, a flexible yet automated support is required. Therefore, we propose that conversational agents (CAs) might be suitable tools for this task. CAs enable the provision of support and information cost-effectively (Kvale etal., 2021) and are used in many educational settings (Okonkwo & Ade-Ibijola, 2021). They can increase learners’ motivation and enable students to access content or receive help swiftly (Okonkwo & Ade-Ibijola, 2021). As a support tool for data exploration, a CA could enable citizens to participate in this research step without producing additional work or costs for initiators, such as personal training or mentoring. Also, it would be a more scalable and constantly available solution, quickly providing citizen scientists with information and assistance to enable their participation and learning. Nevertheless, compared to tutorials and curriculums, CAs can provide personalized support that can adapt to the needs of the individual citizen. CAs require conscious design to fit the audience’s and the domain’s idiosyncratic requirements. Current research on CA design and utilization encompasses aspects of education (e.g., Okonkwo & Ade-Ibijola, 2021; Pérez etal., 2020), working with data (e.g., Alaaeldin etal., 2021; Narechania etal., 2021), and CS (e.g., Holowka etal., 2021; Tavanapour etal., 2019). However, the intersection of these three topics remains a research gap. In particular, while CAs for collecting (e.g., Holowka etal., 2021; Lia etal., 2023; Tavanapour etal., 2019) and accessing data (e.g., Narechania etal., 2021; Neumaier etal., 2017; Simud etal., 2020) have received some scholarly attention, the consecutive use case of analyzing data (i.e., an integral part to strengthen data literacy) has not been explored yet. We hence seek to answer the following research question: RQ: How should a conversational agent be designed to support data exploration in citizen science applications? We address the research question by applying the design science research (DSR) approach. Beyond conveying data literacy, we identify the need for motivation and empowerment of citizens in the literature. Based on this, we derive design principles for a CA supporting citizen participation in data exploration and implement them in a prototypical artifact. Evaluating the prototype in an experimental study, we find that using the CA can enhance data literacy and analysis performance among inexperienced users. With this research, we aim to contribute to the ongoing efforts in reducing information disparities and ensuring that data is leveraged for societal benefit. We further identify opportunities for future research by examining the limitations and challenges of the artifact and our research approach. Related work As the foundation of our work, we review the literature on data literacy and its relationship to CS. We examine existing efforts to support civic engagement in data analysis and explore the potential of CAs in the domains of data literacy, education, and CS to guide our CA design. Data literacy Data literacy can be referred to as “the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, application, and resulting value” (Panetta, 2021, para. 3). Rooting back to the notion of information literacy, with an increasing amount of data and the emergence of more and more data-driven professions, data literacy emerged as a buzzword in research and popular press (Schüller etal., 2019). While Gartner’s definition of data literacy focuses primarily on describing a skill set (Panetta, 2021), other definitions also emphasize the ability and motivation to use these skills in one’s environment. Bhargava etal. (2015) define it as “the desire and ability to constructively engage in society through and about data” (p. 24), and Schüller etal. (2019) describe it as an ability needed to navigate the digitalized Electronic Markets (2024) 34:23 Page 3 of 18 23 world and make informed decisions. Therefore, effective data literacy promotion should not only focus on skills but also on empowering and motivating learners to apply these skills in their respective contexts (Bhargava etal., 2015). To guide teaching and evaluation approaches to data literacy, Schüller etal. (2019) developed a data literacy framework that can be tailored to different needs and requirements. They subdivide data literacy into six core competencies: (1) the establishment of a data culture, (2) the provision of data, (3) the exploitation of data, (4) result interpretation, (5) interpretation of data, and (6) the derivation of actions (Schüller etal., 2019). While the framework provides information about the content required to promote data literacy, it does not address how it can be taught. Examples of teaching approaches to data literacy comprise in-person formats such as workshops (e.g., D’Ignazio, 2022; Debruyne etal., 2021), school initiatives (e.g., Bhargava etal., 2016; Gould, 2021), or online formats such as forums, quizzes, and online classes (Jayawickrama etal., 2020). In addition, many digital tools facilitate data-related tasks, such as data collection, processing, and visualization. D’Ignazio and Bhargava (2016) have mapped tools such as Excel, cartoDB, or infogr.am in view of their flexibility and expertise requirements. However, they pointed out that current tools emphasize output creation rather than learning. They derive four design principles for pedagogical learning tools: targeted focus, guidance, inviting design, and tool expandability (D’Ignazio & Bhargava, 2016). These principles should ensure that tools ease barriers to learning and quickly get users started with activities. While being invited to follow appealing first activities, users should find additional information on more demanding practices (D’Ignazio & Bhargava, 2016). Other authors stress that teaching approaches should encompass multiple pathways for users to choose from— according to their needs (e.g., Bhargava etal., 2015). These should be agile, adaptive, and focus on what is effective and meaningful for the learners, such as working with community data (Bhargava etal., 2015; D’Ignazio, 2022). Citizen science Citizen Science is defined as “the general public engagement in scientific research activities when citizens actively contribute to science either with their intellectual effort or surrounding knowledge or with their tools and resources” (Socientize, 2014, p. 6). Originally used in the natural sciences, today CS has proven useful across many different fields (Pettibone etal., 2017). With the expansion of CS, the heterogeneity of participation approaches has also increased (Shirk etal., 2012; Spasiano etal., 2021). The most common project types are contributory projects that focus on participatory data collection (Bowser etal., 2020; Monzón Alvarado etal., 2020). The participatory analysis and interpretation of data is less common and usually occurs in co-created or collegial CS projects (Shirk etal., 2012). However, citizens have increased access to raw data, for instance, through open (government) data platforms. They could support public institutions in drawing important insights, when given access to (gamified) toolkits supporting data utilization (Krishnamurthy & Awazu, 2016; Simonofski etal., 2022; Wirtz etal., 2022). On the CS platform Zooniverse (www. zooni verse. org), for instance, a strong focus in data analysis is put on participatory image classification (Bonney etal., 2016; Simpson etal., 2014). Moreover, for individual CS projects, digital tools such as Google Spreadsheet are prepared but often used only in a classroom setting, where additional support and teaching are provided (e.g., Kjelvik & Schultheis, 2019; Shah & Martinez, 2016). Within CS projects, learning happens either on a micro level (e.g., through active participation and the execution of tasks) or on a macro level (e.g., by sharing videos or online tutorials) (Jennett etal., 2016). However, the education of citizen scientists and, thus, the provision of educational tools are rarely the focus of CS projects. In a study exploring participant motivation and retention in digital CS projects, Wald etal. (2016) reported that for most projects, scientific outcomes were the focus while “educational and social benefits […] were incidental” (p. 562). For researchers, the main barriers to learning are the necessary temporal, technical, or monetary resources, as well as breaking down complex tasks (Kloetzer etal., 2021). Conversely, participants can be prevented from learning due to a lack of confidence, skills, money, or time (Kloetzer etal., 2021). In addition, project design itself can negatively influence learning by including too little feedback or interaction (Kloetzer etal., 2021). These obstacles should be a starting point for technical solutions supporting participatory data exploration. Conversational agents CAs are applications that allow users to interact with them in a natural language and can either be text or speech-based (Janssen etal., 2020; Rapp etal., 2021). They are also discussed in certain domains under the terms chatbot or chatterbot (Bittner etal., 2019). Offering automation where priory human resources are needed, CAs can be a low-threshold solution, leading to large cost reductions (Kvale etal., 2021). However, maintaining user satisfaction can pose major challenges. Studies on customer service chatbots indicate that factors such as problem resolution, answer precision, and concreteness drive customer satisfaction (e.g., Kvale etal., 2021; van der Goot etal., 2021), while errors and a lack of functionality can quickly deteriorate it (e.g., van der Goot etal., 2021). Likewise, studies on CAs in the workplace suggest that CA adoption depends on user characteristics such as individual tech savviness (e.g., Gkinko & Elbanna, 2023). The possible application fields for CAs range from economics (e.g., finance or e-commerce) to personal applications Electronic Markets (2024) 34:2323 Page 4 of 18 such as health or emotional support (Rapp etal., 2021). It has been shown that good CA design highly depends on the domain it is built for (e.g., Bittner etal., 2019). Further research on the transferability of design knowledge between contexts is necessary (Diederich etal., 2022). For instance, while the usage of social cues is encouraged in some CA applications (e.g., Holowka etal., 2021; Tavanapour etal., 2019), it can have detrimental effects when the reliability of information is essential (Stieglitz etal., 2022). Thus, a one-size-fits-all approach to CA design is unrealistic— context, stakeholders, and unique value propositions must be considered (Janssen etal., 2020). To guide the design of a CA to support citizens in data exploration, different domains provide interesting insights. In the following, we shed light on insights from the application of CAs for education, (big) data-related work and CS (see also Table1 in theelectronicsupplementary material). In the domain of education, research differentiates between teachingand service-oriented CAs. While the former describes CAs targeting knowledge generation, service-oriented CAs provide administrative services, such as introductory or library services (Pérez etal., 2020). When interacting with learners, CAs usually act as “teacher, student, or colleague” (Tamayo-Moreno & Pérez-Marín, 2016, p. 1). In this role, CAs have proven beneficial as they allow for integrating multiple content into one tool and parallel access by multiple users (Okonkwo & Ade-Ibijola, 2021). The possibility of receiving immediate help on demand is convenient with positive effects on learning motivation (Okonkwo & Ade-Ibijola, 2021). CAs also proved suitable for closing learning gaps between mainstream learners and learners from certain minority groups (Pérez etal., 2020). However, through a structured literature review, Pérez etal. (2020) have identified boredom and user frustration (e.g., through lengthy messages and inadequate replies) as common impediments. Teaching CAs can target various topics and domains, with language learning being a prominent use case (Pérez etal., 2020). Another use case, closer related to data literacy, is math education where CAs have been used (e.g., Anh & Ngan, 2021; Nguyen etal., 2019). CAs in data-related work environments usually focus on data provision and depiction for non-technical skilled employees (e.g., Alaaeldin etal., 2021; Narechania etal., 2021; Simud etal., 2020). CAs can conduct tasks such as generating database queries and visualizations in or based on natural language (e.g., Hoon etal., 2020; Narechania etal., 2021; Neumaier etal., 2017; Simud etal., 2020). They can also support decision-making by explaining analytic tools and key performance indicators for a given dataset (e.g., Alaaeldin etal., 2021). Another important application is the identification of relevant datasets, domain-specific scientific tools, and methods (e.g., Keyner etal., 2019; Neumaier etal., 2017; Zhang etal., 2019). Overall, CAs in data-related work environments mainly focus on overcoming modern databases’ technical complexities through natural language interfaces. Since supporting the understanding of data and analysis methods is not the focus of publications, fundamental data literacy remains a prerequisite for users. Within the domain of CS, the usage of CAs for different activities is not yet a common practice. First and foremost, CAs have been used for quantitative and qualitative data collection in CS projects (e.g., Holowka etal., 2021; Isacco etal., 2018; Lia etal., 2023; Tallyn etal., 2018; Tavanapour etal., 2019). They enable participants to answer questionnaires, upload text, pictures, or geotags (e.g., Isacco etal., 2018; Lia etal., 2023; Tallyn etal., 2018) and can, in return, provide guidance or encouragement and support or share data directly with experts or the community (e.g., Holowka etal., 2021). Advantages of their use in data collection can include personalized feedback and the ability to conduct further inquiries when observations are incomplete (Portela, 2021). Additionally, they can provide citizens with data or visualizations (Portela, 2021). Other work explores the advantages of CAs facilitating the ideation process by collecting, structuring, and presenting ideas (e.g., Tavanapour etal., 2019) or supporting the community and its interaction (e.g., Athreya etal., 2018; Portela, 2021). Overall, the potential of using CAs for CS seems to be not yet exploited. For example, we could not identify literature presenting a CA used for training citizen scientists, although the suitability of CAs for educational purposes has been proven in other domains (Okonkwo & Ade-Ibijola, 2021). Research gap The related literature on data literacy, CS, and CAs offers valuable insights into the possibilities and challenges associated with designing activities and tools for enhancing data literacy. However, it underscores a significant research gap at the intersection of these topics: the design of tools for active participation in data exploration. While the data literacy literature provides crucial insights into tool design for learners (e.g., D’Ignazio, 2022; Schüller etal., 2019), it emphasizes the need for more learning-oriented tools embedded in a meaningful context for the user (Bhargava etal., 2015; D’Ignazio & Bhargava, 2016). The CS literature addresses this context and discusses participants’ learning and tools to support projects (e.g., Jennett etal., 2016; Liu etal., 2021; National Academies of Sciences & Medicine, 2018). However, it reveals numerous challenges in integrating educational components (e.g., Kloetzer etal., 2021; Wald etal., 2016) and that the data analysis step is often not addressed. The educational CA literature generally discusses opportunities and challenges in using CAs for teaching (e.g., Okonkwo & Ade-Ibijola, 2021; Pérez etal., 2020), but in the specific context of working with data, the focus remains Electronic Markets (2024) 34:23 Page 5 of 18 23 primarily on discovering datasets (e.g., Keyner etal., 2019; Neumaier etal., 2017; Zhang etal., 2019) or making them accessible (e.g., Hoon etal., 2020; Narechania etal., 2021; Neumaier etal., 2017). Likewise, CAs in CS do not focus on facilitating data analysis but rather center on qualitative or quantitative data collection (e.g., Holowka etal., 2021; Lia etal., 2023; Tallyn etal., 2018). Additionally, they do not exploit their teaching capabilities. Overall, the existing literature provides crucial insights for designing CAs supporting citizens in data exploration, yet specific design guidelines for this use case are missing. Considering the challenges on the transferability of design knowledge across contexts (Bittner etal., 2019; Diederich etal., 2022; Janssen etal., 2020), this represents a significant research gap that should be further explored and investigated. Research approach To answer our research question, we conducted a research project following the DSR approach of Peffers etal. (2020). For a “problem-centered approach” (Peffers etal., 2020, p. 56), the methodology includes six steps, starting with identifying a problem and deriving a motivation for its solution (see Fig.1). First, to elaborate on the problem to solve, we assessed the current state of research by reviewing related literature in the field of data literacy, CS, and CAs and performed a stakeholder analysis based on insights from these domains. In addition, we carried out an expert workshop on data analysis conduction with 12 experts and advanced practitioners, combining ideas from two established requirements elicitation methods “Introspection” and “Brainstorming” (Sharma & Pandey, 2013). While in an introspection, experts elicit the user needs based on their domain knowledge; in brainstorming, participants from different stakeholder groups are invited to collectively generate ideas (Paetsch etal., 2003). The expert workshop facilitated introspection through a think-aloud session about “conducting a data analysis.” Think-aloud sessions, typically known from usability testing, enable researchers to gain insights into participants’ thought processes by neutrally observing them speaking out their thoughts aloud while working on a given task (Ericsson & Simon, 1984; Fan etal., 2020). After the individual think-aloud session, brainstorming was conducted in the expert workshop as an open discussion between all participants, guided by the components of the data literacy framework (Schüller etal., 2019). The approach to the conduct of the expert workshop is further described in the “Expert workshop (activity 1)” section. Second, the following activity of the DSR approach comprised the definition of solution objectives by deriving either quantitative or qualitative requirements for the artifact (Peffers etal., 2020). Therefore, we translated the results from the first activity into atomic user needs as solution objectives and positioned them in the related literature. Thirdly, we approached the actual design and development phase by instantiating our artifact. The two tasks of this phase were outlining the necessary function and design requirements before practically creating the artifact (Peffers etal., 2020). Using our user needs, we determined design specifications, following the schema by Gregor etal. (2020), and implemented them in a prototypical instantiation. The approach to specifying the design principles and implementing the artifact is further described in the “Design principles for a conversational agent for public participation in data analysis” and “Artifact” sections, respectively. To close the first DSR cycle, the artifact should be demonstrated and evaluated. We covered these steps simultaneously by designing and conducting a final experiment with 30 participants guided by best practices to evaluate CAs and data literacy learning (Pérez etal., 2020; Schüller etal., 2019) and DSR artifacts (Venable etal., 2016). Using a between-subjects design, the experiment compared the data Fig. 1 Overview of the DSR approach Electronic Markets (2024) 34:2323 Page 6 of 18 analysis performances and (learning) experiences of a participant group guided by the designed artifact with the results of a group that received no explicit guidance but was allowed to use existing material through the web. By combining quantitative (task performance, empowerment, motivation, perceived learning, user experience) and qualitative (user experience, dialog analysis) insights, the experiment enabled us to evaluate the artifact comprehensively. The approach to the experiment’s design, conduction, and evaluation is described in detail in the “Evaluation” section. Designing aconversational agent forpublic participation indata analysis Problem awareness andsolution objectives We set out to design a CA capable of supporting data exploration in CS projects. This design endeavor entails understanding the intricacies of data literacy and CS and applying this knowledge to CAs. The review of relevant literature has shown that fostering data literacy requires creative teaching approaches that consider the interests and realities of the target audience (e.g., Bhargava etal., 2015; D’Ignazio, 2022). A challenge in this regard is the complexity of the above concepts and that data literacy is based on a set of competencies rather than a specific skill or technique (Debruyne etal., 2021; Schüller etal., 2019). The intended data literacy content thus needs to be broken down to guide the design of the CA (activity 1). While data collection is frequently seen in CS projects (Bowser etal., 2020; Liu etal., 2021; Monzón Alvarado etal., 2020), few projects use participatory data analysis (beyond classification tasks). In general, budgets and time constraints limit such projects for researchers and citizens (Kloetzer etal., 2021; Wald etal., 2016). In addition, maintaining momentum and keeping citizens engaged and active (beyond the first exploration) are crucial but difficult (Wald etal., 2016). Therefore, understanding citizen scientists is key to the design of appropriate support tools (activity 2). Expert workshop (activity 1) To understand the specific requirements of the CA (content and design), we invited 12 data analysts first to an individual think-aloud session and second to a brainstorming session. The participants consisted of six Ph.D. candidates and six Table 1 Atomic user needs grouped by perspective and contrasted with related literature User needs derived from the workshop Concepts in the literature As a student, I want to… U1 Understand the data analysis process Different activities in data value creation (Schüller etal., 2019) U2 Understand the dataset (meaning, usefulness) Competence obtain data, prepare data (Schüller etal., 2019) U3 Know and apply methods of data cleaning U4 Find a start for the data analysis Low entry point for data literacy tools (D’Ignazio & Bhargava, 2016) U5 Understand and select a method for data analysis Competence analyze data, interpret data analysis, visualize data, interpret data visualizations (Schüller etal., 2019) U6 Note limits and challenges of the data analysis U7 Interpret analysis results and critically question findings U8 Select and design appropriate data visualizations U9 Understand how to interpret and check visualizations U10 Ask questions CA’s answering student’s questions (Okonkwo & AdeIbijola, 2021) U11 Decide on an analysis action and path Enabling multiple pathways for learners to choose from (Bhargava etal., 2015) U12 Get access to data analytics tools Knowledge and mastery of tools as essential skills (Schüller etal., 2019) U13 Get access to assistance and helpful material CA’s integrating multiple contents (Okonkwo & AdeIbijola, 2021) As a tutor, I want to… U14 Steer through the analysis process Guided data literacy tools (D’Ignazio & Bhargava, 2016) U15 Point out missing competencies and skills - U16 Proceed with questions (answer, redirect, collect) CA’s answering student’s questions (Okonkwo & AdeIbijola, 2021) U17 Form an interface to other material/ software Expandable data literacy tools (D’Ignazio & Bhargava, 2016) U18 Support different levels of pre-knowledge CAs providing individualized support (Okonkwo & Ade-Ibijola, 2021) Electronic Markets (2024) 34:23 Page 7 of 18 23 master students majoring in data science-related fields and presented thus advanced to professional practitioners in the field. Sessions were conducted virtually using an online conference tool moderated by one researcher, which had some implications for the workshop conduction. In the think-aloud session, participants were asked to solve several analytical tasks based on a given dataset. Instead of being able to observe their actions physically, in the virtual thinkaloud sessions, we used audio, video, and screen sharing to enable as many insights as possible for the researcher. In the following group brainstorming session, participants could then discuss approaches and pitfalls to data exploration and requirements for support based on their own experiences in the think-aloud session. Since in virtual compared to onsite groups lower social presence can be a challenge to the discussion quality, we limited the size of the brainstorming groups to three participants per session to better integrate the individual participants and utilized an online whiteboard to facilitate collaboration (Roberts etal., 2006). The results of the expert workshop were translated into atomic user needs of students in data exploration and tutors in student support and contextualized with the literature review results (see Table1). Stakeholder analysis (activity 2) Most CS initiatives cater to broad audiences (Spiers etal., 2019). Common user characteristics include above-average education, above-average income, and above-average seniority (Ciarán Mac Domhnaill & Nolan, 2020; National Academies of Sciences & Medicine, 2018). In addition, citizen scientists tend to “embody the characteristics of autonomy, competence, and relatedness in their hobby” (Jones etal., 2018, p. 15). However, CS embraces the diversity of participants, and project organizers claim to strive for more diversity in terms of age, gender, and ethnicity (National Academies of Sciences & Medicine, 2018). Thus, the level of autonomy and knowledge can be assumed to be heterogeneous. Based on this and research on data literacy (e.g., Logan, 2017; Watson & Callingham, 2004), we distinguish the following (broad) user groups to have in mind for the CA artifact: (1) beginner users, (2) advanced users, and (3) professional users (see Table2). Design principles foraconversational agent forpublic participation indata analysis A design principle should include an aim, implementer, and user; a context; mechanisms; and a rationale (Gregor etal., 2020, p. 1634). We follow this scheme and propose five principles for the design of a CA for support in data analysis tasks (context), which can be used by researchers and developers (implementer) to create software support for non-expert citizen scientists undertaking their analytical activities (users). Considering user needs U1, U11, U14, and U18, the platform must specify a certain process structure while the user remains free to choose how to follow this path. We find guidance for this requirement in the design principles of Tavanapour etal. (2019), who state that a CA for idea creation must be able to follow a given conversation flow while still being able to lead the process actively and D’Ignazio andBhargava(2016), who underlined, a data literacy tool should be guided. Additionally, Portela (2021) advises that a CA should include fixed chat commands for user orientation. Therefore, we formulate the first design principle as follows: DP1: In order to structure the analysis process (aim), the system should provide a sorted menu highlighting the individual parts of a data analysis (mechanism), as this enables the user to get guidance on the process and navigate to a specific topic of interest (rationale). Several user needs (U2, U4) express that beginners’ entrance must be eased. Thus, the system should “provide a low entry point” (p. 87) for data analysis (D’Ignazio & Bhargava, 2016). Nevertheless, the knowledge needed to conduct many such tasks is comprehensive (U3, U5, U7, U8, U9). The stakeholder analysis showed the need to account for different user groups, which is supported by Bhargava etal. Table 2 Description of user groups for the CA artifact User groups Description Beginner users No to little knowledge about data literacy (see idiosyncratic level, informal level (Watson & Callingham, 2004)) New to CS or without the typical characteristics of an experienced citizen scientist, requiring guidance and explanations Advanced users Experienced citizen scientists or people experienced in data literacy with a fundamental understanding of the data process (see conversational level; Logan, 2017), data analysis, and visualization methods (see inconsistent and consistent noncritical level; Watson & Callingham, 2004) Knowledge is rather basic and incomplete or might date back a long time ago, but users are more autonomous and inform themselves or might have specific questions Professional users Familiar with working with data (see critical and critical mathematical level; Watson & Callingham, 2004) and working largely to completely autonomously Group is rather out of scope for the CA Electronic Markets (2024) 34:2323 Page 8 of 18 (2015), pointing out the necessity to provide “multiple pathways for people with different data literacy needs and capacities to interact within a complex system” (p. 15). Therefore, the platform should equip users with the appropriate background knowledge based on their needs and interests. Tavanapour etal. (2019) to this end propose a comparable mechanism specifying that CAs should have the “capacity to summarize […] information […] and offer further explanations, if requested” (p.8). We, therefore, formulate our second design principle as follows: DP2: The system should provide a tiered knowledge structure (mechanism) to educate the user efficiently (aim), as this enables the user to determine the depth according to their interests and skills (rationale). User needs U10 and U16 express that the users should be enabled to get answers to their specific questions, which is a common functionality for teaching CAs (Okonkwo & AdeIbijola, 2021). We, thus, formulate the third design principle as follows: DP3: The system should allow users to enter questions and process them (mechanism) to get answers to individual questions (aim, rationale). User needs U12, U13, and U17 imply that the platform should use existing teaching material. To this end, D’Ignazio andBhargava(2016) point out that data literacy tools should be expandable, bridging the pathway for learners to go from one data literacy tool to the other. We incorporate these findings in the fourth design principle: DP4: To efficiently educate the user (aim), the system should provide a combination of self-developed and external materials through embedding or forwarding (mechanism), as users have an interest in a broad offer of learning material (rationale). Furthermore, the presence of many pitfalls (user needs U6, U9, U15) requires the platform to support users in understanding challenges and avoiding common mistakes. We, therefore, propose that: DP5: The system should provide indications and warnings of challenges and common mistakes in time (mechanism) to prevent the user from failing (aim, rationale). An overview of the design principles and their derivation from the atomic user needs are found in Table3. Artifact In the third phase of the DSR process, the formulated design principles are instantiated in an artifact in the form of a CA prototype. The CA provides dataset-independent support to beginner and advanced users in the process of data analysis. It provides process-oriented and knowledge-based advice through messages, pictures, links, and guidance along two workflows: Workflow 1 The data analysis (DA) workflow offers guidance for beginners and provides a menu of steps (Fig.2), showing different steps of a typical data analysis process (DP1). The menu serves as a central point to which the user returns within the flow. The first step of the DA process (“Getting started”) reflects DP2, DP4, and DP5. After receiving information on how to get started, the user can request more information (DP2) or browse through external education material (DP4). To address DP5, the CA invites users to analyze their data actively. Upon the user’s confirmation to proceed, the bot provides an overview of common mistakes concerning the task the user has just completed (DP5). Workflow 2 The question and answer (Q&A) workflow should attract users with basic data knowledge. Here, users can specify topics of interest by asking questions. Upon a request, the CA either recognizes the question as dataset-specific or methodological. In the former case, the bot points out that such questions are out of scope. In the latter case, the bot provides an answer if it recognizes the question. If the question is not recognized, the CA offers to forward the question to a supervising researcher. Upon affirmation, the CA sends questions and contact details to an online spreadsheet, privately accessible to the supervising researcher. Table 3 Design principles mapped to their respective user needs Design principles Associated user needs DP1 Provide a menu that structures the analytics process for the user to get guidance or navigate to a specific topic U1, U11, U14, U18 DP2 Provide a tiered knowledge structure to let the user determine the depth according to their interests and skills U2, U3, U4, U5, U7, U8, U9 DP3 Allow the users to enter questions and process them by either ad hoc answering or forwarding U10, U16 DP4 Provide a combination of self-developed and external materials through embedding or forwarding to educate the user efficiently U12, U13, U17 DP5 Provide indications and warnings of challenges and common mistakes in time U6, U9, U15 Electronic Markets (2024) 34:23 Page 15 of 18 23 knowledge. Applied to our experiment, this could mean the amount of knowledge presented to the CA users could have negatively affected the user’s assessment of aspects such as his or her feeling of empowerment or learning through the analysis activities, compared to a user not exposed to this knowledge. A third research string could focus on evaluating the CA in a real-life CS environment, including assessing who can use it and who is excluded. This perspective would be critical as CS strives for inclusivity (National Academies of Sciences & Medicine, 2018; Sorensen etal., 2019), and the underor over-representation of particular groups can have negative consequences for project outcomes (Sorensen etal., 2019). Therefore, understanding who is excluded from our artifact and what alternatives could be created would be indispensable. Conclusion Inequalities in data access and literacy pose a risk to the individual and society as a whole. In this work, we therefore investigated the use case of CS as a means to empower citizens in accessing and working with data. We have presented results from the first cycle of a DSR project targeting the development of a CA to support data exploration in CS projects. Following the six steps for a problem-centered design process by Peffers etal. (2020), we have approached this challenge by structuring and elaborating on the problem space and its associated stakeholders, eliciting requirements and translating them into design principles for a solution artifact and finally presenting and testing a prototypical implementation of this artifact. The result of our first design cycle is a CA for data analysis activities offering flexible support on demand to multiple users in parallel. For inexperienced users, the tool provides seamless guidance through data analysis by providing dataset-independent knowledge and tips, allowing users to decide how deeply they want to dive into a particular topic. Advanced users can use a question-and-answer process to ask questions about data analysis freely, thus enriching only their knowledge with the CA and controlling the analysis process. In its current state, the CA shows high potential for transferring required data literacy to citizens, enabling them to perform better in analytical tasks. Qualitative user feedback shows that multiple citizens perceive the tool’s support as enjoyable and useful and point out potential application fields. Harnessing the advantages of their easy, resourceefficient provisioning, the usage of CAs in CS projects seems promising and could positively promote equitable access to data-driven knowledge. However, identified challenges, such as the participants’ motivation, feeling of empowerment, and perceived learning effect, could not be solved adequately by the CA. This indicates that further research is necessary to refine the CA and its usability, which we intend to accomplish in a proceeding design cycle. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1252502400705-3. Acknowledgements We want to thank the 12 workshop participants and the 30 experiment participants for their contribution to our work. Funding Open Access funding enabled and organized by Projekt DEAL. Data Availability The data that support the findings of this study are available from the authors upon reasonable request. Declarations Conflict of interest The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Alaaeldin, R., Asfoura, E., Kassem, G., & Abdel-Haq, M. S. (2021). 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