A taxonomy for deriving business insights from user-generated content

Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS

Design and Evaluation of an AI-based Learning System to Foster Students\u27 Structural and Persuasive Writing in Law Courses

Structured and persuasive writing is essential for effective communication, convincing readers of argument validity, and inspiring action. However, studies indicate a decline in students\u27 proficiency in this area. This decline poses challenges in disciplines like law, where success relies on structured and persuasive writing skills. To address these issues, we present the results of our design science research project to develop an AI-based learning system that helps students learn legal writing. Our results from two different experiments with 104 students demonstrate the usefulness of our fully working AI-based learning system to support law students independent of a human instructor, time, and location. In addition to providing our embedded software artifact, we document our evaluated design knowledge as a design theory. Thus, we provide the first step toward a nascent design theory for the development of AI-based learning systems for legal writing

Supporting Human Cognitive Writing Processes: Towards a Taxonomy of Writing Support Systems

In the field of natural language processing (NLP), advances in transformer architectures and large-scale language models have led to a plethora of designs and research on a new class of information systems (IS) called writing support systems, which help users plan, write, and revise their texts. Despite the growing interest in writing support systems in research, there needs to be more common knowledge about the different design elements of writing support systems. Our goal is, therefore, to develop a taxonomy to classify writing support systems into three main categories (technology, task/structure, and user). We evaluated and refined our taxonomy with seven interviewees with domain expertise, identified three clusters in the reviewed literature, and derived five archetypes of writing support system applications based on our categorization. Finally, we formulate a new research agenda to guide researchers in the development and evaluation of writing support systems

A speech-based empathy training system - initial design insights

Empathy is an essential component of human communication since it increases our understanding and perception of others. However, studies show that students\u27 empathy skills have declined rapidly in the last decades. Against this background, practitioner reports predict that the importance of empathy will increase as a skill for successful agile teamwork in the future. Therefore, researchers have designed information systems to train empathy abilities of learners in different domains. Nevertheless, research on automated speech-based training is rather scarce. Hence, we aim to investigate how to design a speech-based empathy training system that helps students react emotionally adequately in communication. This research in progress paper presents five initial requirements that guide future research and development of a speech-based empathy training system intended to support students\u27 self-regulated learning. With this, we hope to provide guidance for the design and embedding of speech-based empathy training systems at scale

Emotion-Adaptive Learning Systems for Transferrable Skill Learning

In traditional school education, acquiring factual knowledge is still the major focus. The advancement in artificial intelligence (AI) and the digitalization of socio-economical processes, however, have fundamentally changed the way humans work and live. Today, humans have the ability to access facts fast through smartphones, computers, and the internet. They can access this knowledge flexibly whenever they want. Thus, obtaining factual knowledge by heart has become less essential. In a fast-paced world, individuals must quickly adapt to new environments. Transferrable skills have become essential as they support humans to adapt to dynamic environments. They are seen as a key catalyst for helping the workforce adapt (ILO, 2021)

Narrow optical linewidths and spin pumping on charge-tunable close-to-surface self-assembled quantum dots in an ultrathin diode

We demonstrate full charge control, narrow optical linewidths, and optical spin pumping on single self-assembled InGaAs quantum dots embedded in a 162.5−nm-thin diode structure. The quantum dots are just 88nm from the top GaAs surface. We design and realize a p−i−n−i−n diode that allows single-electron charging of the quantum dots at close-to-zero applied bias. In operation, the current flow through the device is extremely small resulting in low noise. In resonance fluorescence, we measure optical linewidths below 2μeV, just a factor of 2 above the transform limit. Clear optical spin pumping is observed in a magnetic field of 0.5T in the Faraday geometry. We present this design as ideal for securing the advantages of self-assembled quantum dots—highly coherent single-photon generation, ultrafast optical spin manipulation—in the thin diodes required in quantum nanophotonics and nanophononics applications

EMQN best practice guidelines for the molecular genetic testing and reporting of chromosome 11p15 imprinting disorders: Silver–Russell and Beckwith–Wiedemann syndrome

Molecular genetic testing for the 11p15-associated imprinting disorders Silver–Russell and Beckwith–Wiedemann syndrome (SRS, BWS) is challenging because of the molecular heterogeneity and complexity of the affected imprinted regions. With the growing knowledge on the molecular basis of these disorders and the demand for molecular testing, it turned out that there is an urgent need for a standardized molecular diagnostic testing and reporting strategy. Based on the results from the first external pilot quality assessment schemes organized by the European Molecular Quality Network (EMQN) in 2014 and in context with activities of the European Network of Imprinting Disorders (EUCID.net) towards a consensus in diagnostics and management of SRS and BWS, best practice guidelines have now been developed. Members of institutions working in the field of SRS and BWS diagnostics were invited to comment, and in the light of their feedback amendments were made. The final document was ratified in the course of an EMQN best practice guideline meeting and is in accordance with the general SRS and BWS consensus guidelines, which are in preparation. These guidelines are based on the knowledge acquired from peer-reviewed and published data, as well as observations of the authors in their practice. However, these guidelines can only provide a snapshot of current knowledge at the time of manuscript submission and readers are advised to keep up with the literature