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Chatbots are becoming a ubiquitous trend in many fields such as medicine, product and service industry, and education. Chatbots are computer programs used to conduct auditory or textual conversations. A growing body of evidence suggests that these programs have the potential to change the way students learn and search for information. Especially in large-scale learning scenarios with more than 100 students per lecturer, chatbots are able to solve the problem of individual student support. However, until now, there has been no systematic, structured overview of their use in education. The aim of this paper is therefore to conduct a systematic literature review based on a multi-perspective framework, from which we have derived initial search questions, synthesized past research, and highlighted future research directions. We reviewed titles and abstracts of 1405 articles drawn from management, education, information systems, and psychology literature before examining and individually coding a relevant subset of 80 articles.The results show that chatbots are in the very beginning of entering education. Few studies suggest the potential of chatbots for improving learning processes and outcomes. Nevertheless, past research has revealed that the effectiveness of chatbots in education is complex and depends on a variety of factors. With our literature review, we make two principal contributions: first, we structure and synthesize past research by using an input-process-output framework, and secondly, we use the framework to highlight research gaps for guiding future research in that area.

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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 IS. However, IS research on trust has mainly focused on the trust relationship between the user and the IS itself, largely neglecting that other targets of trust might also drive IS use from a user's point of view. Accordingly, we investigate the importance of different targets of trust in IS use. Therefore, we use the concept of a network of trust and identify four different targets of trust that are prevalent from a user's point of view. Afterwards, we develop our research model and evaluate it using a free simulation experiment. The results show that multiple targets of trust are important in the context of IS use. In particular, we highlight the importance of a second target -trust in the provider -which is equally important as trust in the IS itself. Consequently, IS providers should focus not only on fostering users' trust in their IS but also on positioning themselves as trustworthy providers. In addition, we show that a third target -trust in the Internet -has significant indirect effects on multiple constructs that impact IS use.

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.

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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.

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