Conversational agents (CAs) are increasingly being adopted across various domains, with enterprises particularly interested in leveraging these technologies to explore new opportunities. CAs, such as Copilot, are transforming human-computer collaboration in the workplace by serving as effective work assistants. Additionally, their advanced natural language understanding capabilities position them as powerful tools for facilitating crowdsourcing within enterprises.
This thesis investigates the dual role of CAs in enterprises: as work assistants for employees and as facilitators of crowdsourcing. By acting as work assistants, CAs interact with a broad range of employees, making them well-suited to gather valuable insights that contribute to the company’s knowledge base and support collaborative problem-solving. The central research question explores how to effectively engage and motivate employees in this crowdsourcing process through CA personality design, which is relatively unexplored. Drawing on the "Computers Are Social Actors" (CASA) framework by Nass et al. (1994), this thesis examines CA personality design, aiming to enhance human-CA interaction by eliciting social responses from employees and thereby increasing their engagement.
We approached these research questions from three perspectives: (1) designing and specifying CA personalities for effective implementation in enterprises, (2) identifying the most and least promising qualities of CA personality designs in this context, and (3) designing an empirical study within the enterprise to measure which personality archetype design is the most effective.
We reviewed literature on the context of conversational crowdsourcing in enterprise settings, and CA design methods with a focus on CA anthropomorphism. These findings informed the initial design phase, where CA personality archetypes were tested in an enterprise environment. Based on these insights, we developed design guidelines for enterprise CA personality design. After refining the archetypes, we proposed three unique CA personalities aimed at enhancing employee participation in enterprise crowdsourcing through their interactions with CAs. These archetypes were then implemented in the enterprise's internal chat platform for an experiment, where employee interactions with the CAs were collected and analysed both quantitatively and qualitatively.
The results of our experiment reveal how different CA personalities impact user engagement in enterprise crowdsourcing, demonstrating how distinct CA personalities can be effectively translated into LLM prompts and perceived by users. The study also highlights the relationship between CA personality and the triggering of users' social responses, as observed through sentiment analysis. Additionally, our research identifies the challenge of balancing social engagement with maintaining professionalism in enterprise CAs to build user trust.
This thesis concludes with a discussion of the findings and their implications for future research in enterprise CA design.

Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled health professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. In this thesis we evaluate the state-of-the-art in automatic text simplification in the medical domain. We train a Neural Machine Translation (NMT) system on aligned complex and simple sentences from Wikipedia and Simple Wikipedia. As there are no publicly available aligned medical text simplification corpora, we create one semi-automatically with the help of a domain expert and one fully automatically using a novel monolingual alignment method introduced in this thesis. We analyse the effect of in-domain data when training an NMT system. Furthermore, we describe two strategies for medical term simplification in combination with NMT: 1) An extra pre-processing step that boosts medical term simplification 2) A post-processing dictionary approach using the Open-Access and Collaborative Consumer Health Vocabulary (CHV). We analyse the effect of both strategies separately. We let humans evaluate the output on grammar, meaning preservation (from the complex sentence) and simplicity (compared to the complex sentence).

Results show that an NMT trained on general aligned complex and simple sentences is able to simplify medical sentences at the level of Simple Wikipedia. An NMT trained on medical sentences (in addition to general sentences) in combination with the boosting strategy for medical term simplification is able to translate more medical concepts, but the output is not simpler than the NMT trained on general sentences only. Interestingly, NMT in combination with the CHV did not boost simplicity, but had the opposite effect.