Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for many learners. Recent advances in machine learning (ML) have shown the potential to support learners in complex writing tasks. To test the effects of ML-based support on students’ legal writing skills, we developed the intelligent writing support system LegalWriter. We evaluated the system’s effectiveness with 62 students. We showed that students who received intelligent writing support based on their errors wrote more structured and persuasive case solutions with a better quality of legal writing than the current benchmark. At the same time, our results demonstrated the positive effects on the students’ writing processes.

We introduce an argumentation annotation scheme that models basic argumentative structure and additional contextual details across diverse user opinion domains. Drawing from established argumentation modeling approaches and related theory on user opinions, the scheme integrates the concepts of argumentative components, specificity, sentiment and aspects of the user opinion domain. Our freely available dataset includes 1,016 user opinions with 7,266 sentences, spanning products from 19 e-commerce categories, restaurants, hotels, local services, and mobile applications. Utilizing the dataset, we trained three transformer-based models, demonstrating their efficacy in predicting the annotated classes for identifying argumentative statements and contextual details from user opinion documents. Finally, we evaluate a prototypical dashboard that integrates the model inferences to aggregate information and rank exemplary products based on a vast array of user opinions. Early results from an experimental evaluation with eighteen users include positive user perceptions but also highlight challenges when condensing detailed argumentative information to users.

New political objectives, emerging regulatory regimes for the digital sphere, and higher penalties for violations have intensified the pressure to develop lawful IT artefacts. As the adaptation of existing IT artefacts to new regulations can be expensive and arduous, a more attractive approach would be to design IT artefacts lawfully from the beginning. A major challenge is that the law is generally technology-neutral, and lawful design requires legal expertise throughout the development, which is costly and time consuming due to communication challenges between legal experts and developers. One possible approach to proactively consider IT regulations in the systems development is design patterns that convey legal design knowledge and support developers in determining the appropriate design options. Consequently, we develop a framework for lawful design patterns and demonstrate their feasibility and advantages using the example of developing AI-based assistants and the regulation of the General Data Protection Regulation (GDPR). Using the design pattern framework, we develop design patterns for lawful AI-based assistants and evaluate them using (a) an experimental approach to show the usefulness of the patterns for developers and (b) rely on a legal simulation study to holistically evaluate how design patterns contribute to lawful IT.

This paper employs a longitudinal perspective to examine continued system use (CSU) by individuals in utilitarian, volitional contexts when alternative systems are present . We focus on two key behavioural antecedents of CSU – habit and continuance intention – and theorise how the relationships between CSU and these antecedents evolve over time. In addition, we hypothesise how the interaction effect of habit and intention on CSU evolves temporally. Our theorising differs from extant literature in two important respects: 1) In contrast to the widespread acceptance of the diminishing effect of continuance intention on CSU in the information systems (IS) literature, we hypothesise that in our context, its impact increases with time; and 2) In contrast to the negative moderation effect of habit on the relationship between intention and CSU proposed in the literature, we posit a positive interaction effect. We collect longitudinal survey data on the use of a higher education IS from students in a European university. Our results suggest that the impact of continuance intention on CSU as well as the interaction effect between habit and intention are increasing over time. We further introduce a methodological innovation – the permutation approach to conduct the multi-group analysis with repeated measures – to the literature.

Advances in Natural Language Processing offer techniques to detect the empathy level in texts. To test if individual feedback on certain students’ empathy level in their peer review writing process will help them to write more empathic reviews, we developed ELEA, an adaptive writing support system that provides students with feedback on the cognitive and emotional empathy structures. We compared ELEA to a proven empathy support tool in a peer review setting with 119 students. We found students using ELEA wrote more empathic peer reviews with a higher level of emotional empathy compared to the control group. The high perceived skill learning, the technology acceptance, and the level of enjoyment provide promising results to use such an approach as a feedback application in traditional learning settings. Our results indicate that learning applications based on NLP are able to foster empathic writing skills of students in peer review scenarios.

Conversational agents (CAs) provide opportunities for improving the interaction in evaluation surveys. To investigate if and how a user-centered conversational evaluation tool impacts users' response quality and their experience, we build EVA - a novel conversational course evaluation tool for educational scenarios. In a field experiment with 128 students, we compared EVA against a static web survey. Our results confirm prior findings from literature about the positive effect of conversational evaluation tools in the domain of education. Second, we then investigate the differences between a voice-based and text-based conversational human-computer interaction of EVA in the same experimental set-up. Against our prior expectation, the students of the voice-based interaction answered with higher information quality but with lower quantity of information compared to the text-based modality. Our findings indicate that using a conversational CA (voice and text-based) results in a higher response quality and user experience compared to a static web survey interface.

Thiemo Wambsganss, Christina Niklaus, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Techniques from Natural-Language-Processing offer the opportunities to design new dialog-based forms of human-computer interaction as well as to analyze the argumentation quality of texts. This can be leveraged to provide students with adaptive tutoring when doing a persuasive writing exercise. To test if individual tutoring for students’ argumentation will help them to write more convincing texts, we developed ArgueTutor, a conversational agent that tutors students with adaptive argumentation feedback in their learning journey. We compared ArgueTutor with 55 students to a traditional writing tool. We found students using ArgueTutor wrote more convincing texts with a better quality of argumentation compared to the ones using the alternative approach. The measured level of enjoyment and ease of use provides promising results to use our tool in traditional learning settings. Our results indicate that dialog-based learning applications combined with NLP text feedback have a beneficial use to foster better writing skills of students.

Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students' argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.

Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffolding learners' understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language.We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews.We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme.The obtained interrater agreement of α = 0.57 for argument components and α = 0.49 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement.Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.

Gamification is a well-known approach that refers to the use of elements to increase the motivation of information systems users. A remaining challenge in gamification is that no shared understanding of the meaning and classification of gamification elements currently exists. This impedes guidance concerning analysis and development of gamification concepts, and often results in non-effective gamification designs. The goal of our research is to consolidate current gamification research and rigorously develop a taxonomy, as well as to demonstrate how a systematic classification of gamification elements can provide guidance for the gamification of information systems and improve understanding of existing gamification concepts. To achieve our goal, we develop a taxonomic classification of gamification elements before evaluating this taxonomy using expert interviews. Furthermore, we provide evidence as to the taxonomy’s feasibility using two practical cases: First, we show how our taxonomy helps to analyse existing gamification concepts; second, we show how our taxonomy can be used for guiding the gamification of information systems. We enrich theory by introducing a novel taxonomy to better explain the characteristics of gamification elements, which will be valuable for both gamification analysis and design. This paper will help guide practitioners to select and combine gamification elements for their gamification concepts.

What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.

Technology acceptance research has shown that trust is an important factor fostering use of information systems (IS). As a result, numerous IS researchers have studied factors that build trust in I...