LectureChat extends the WikiChat conversational AI system by integrating multilingual university lecture transcripts alongside

Wikipedia content. The demo showcases a dual retrieval architecture that combines structured encyclopedic knowledge with academic lecture

material, leveraging multiple segmentation strategies and cross index reconciliation to improve retrieval quality. The system maintains separate

citation spaces for Wikipedia (numeric) and lectures (alphabetic) and preserves temporal provenance for direct video navigation. We present

the overall architecture, interaction flow, implementation details, and a reproducibility plan.

Progress in artificial intelligence research, caused by the volume of available data on the World Wide Web, the development of affordable yet extremely efficient mathematical processors ("GPUs", graphical processing units), and the discovery of more effective training algorithms for very large models (such as "transformer" neural networks like Google's BERT and OpenAI's GPT, the technology behind ChatGPT.com) has recently led to a technological convergence that has begun to disrupt many other areas of scientific research, business and life. In this work, we explore some methodological concerns and boundary conditions when aspiring to apply such advanced technologies in order to advance the state of the art in software-implementable models for risk intelligence. We look at the potential of these technologies to assist open-ended 360˚ risk profiling, ethical and government questions such as dealing with the inherent bias in data, potentially unknown status of of information’s factuality of datasets, questionable provenance of datasets and other factors, such as sabotaging models. Borrowing from security engineering, we adopt the concept of the ‘attack surface‘ and introduce a variant of it as ‘risk surface‘: we posit that a good risk model should be supplemented by a model of its own risks in the form of making limitations like blind spots and known questionable behavior explicit. Model cards a proposed as a standard type of document to capture the risk profile of the risk model itself.

In this short experience report, we present our attempt to integrate output from an ongoing research project with a traditional, mostly literature-based, course on media manipulation, bias and fake news, to turn an existing undergraduate course into a more engaging experience for attending students. To this end, we successfully utilized our system BiasScanner for news bias detection and classification (BiasScanner.org).

I present a couple of active research projects of my Information Access Research Group (IARG) that include work on the detection, sub- classification and explaining of sentence-level news bias, methodology support for machine learning projects, as well as RAG chatbots that assist with areas ranging from machine learning project work in a company to students learning about artificial intelligence; I conclude with some thoughts on evaluation.

Die Rolle des Denkens im KI-Zeitalter

Media bias is an enduring feature of news dissemination, reflecting the subjective perspectives of its creators across history. From archaic records like "The Victory Stele of Naram-Sin" to contemporary news channels, bias permeates media, influencing political, social, and public health narratives. This research aims to investigate the persistent phenomenon of media bias and the potential of large language models (LLMs)(Kojima et al., 2022) in its detection and classification, in order to deploy publicly available software tools aiming to enhance media literacy among news consumers.

Traditionally, media bias served the interests of ruling powers; even with the rise of modern journalism, objectivity is often compromised by commercial pressures and inherent human biases. (Rodrigo-Ginés et al., 2024). As media landscapes evolve, bias continues to shape public opinion, impacting democratic processes and public health perceptions—evident during the COVID-19 pandemic, where polarized media narratives swayed public health decisions and fueled misinformation. (Recio-Román et al., 2023)

Current research on the effects of labeling media bias or propaganda, whether automatically or with human involvement, highlights the complexity of the issue. Depending on different circumstances, labeling can lead to negative outcomes (such as reinforcing filter bubbles by providing means to avoid news with a different perspective), no change in news consumption behavior at all, or, in some cases, an actual improvement in media literacy as intended (Zavolokina et al., 2024).

This research aims to develop a technical solution for the automatic labeling of biased media content, emphasizing several proposals that we hope will lead to a positive effect on media literacy among those presented with the system’s assessments.

These proposals include using a fine-grained taxonomy of bias types rather than a simple binary left/right labeling, focusing on detailed explanations for each model decision in natural language, marking bias at the sentence level rather than at the article or publication level to provide more insights, fine-tuning autoregressive models like GPT-3.5 or Mistral with high-quality examples instead of using “simple” bidirectional models like BERT(Brown et al., 2020) or non-finetuned models, and focusing on the German language, which has not yet been properly explored for such systems.

Understanding readers' perceptions when exposed to bias-labeled content is another facet of this research. It will explore how bias labeling influences readers' views on credibility and neutrality and whether real-time bias indicators affect news consumption behaviors. As mentioned, practical applications serve as a cornerstone of this research. One aim is to implement bias detection systems in real-world settings, such as search engines and news aggregators, to promote balanced information consumption. The development of user tools, like browser extensions highlighting media bias, intends to address public need for transparent information evaluation.

In essence, this research contributes to media literacy enhancement by demystifying media bias through advanced computational methods. By refining detection mechanisms, classifying bias more effectively, and implementing practical tools, it aims to fortify democratic discourse and public understanding, thereby addressing the pervasive influence of media bias in today’s interconnected world.

References

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.

Recio-Román, A., Recio-Menéndez, M., & Román-González, M. V. (2023). Influence of Media Information Sources on Vaccine Uptake: The Full and Inconsistent Mediating Role of Vaccine Hesitancy. Computation (Basel). https://doi.org/10.3390/computation11100208

Rodrigo-Ginés, F.-J., Carrillo-de-Albornoz, J., & Plaza, L. (2024). A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it. Expert Systems with Applications, 237, 121641.

Keywords: Media Bias, Large Language Models, Bias Detection, Natural Language Processing, Journalism, Public Opinion, Taxonomy

Learning is at the heart of every progress the human species makes. It is most effective when it considers who we are as individuals, what learning approach we prefer and what we already know to begin with. In the digital age, we strive to capture such information in the form of a digital representation -- the so-called learner model --, to tailor learning-related systems to this information and build upon it to create more personalised learning experiences. Over recent years, the proliferation of diverse models across various educational applications and disciplines has made it challenging to access targeted research.

In this survey, we aim to address this gap, reviewing the latest advances in learner modelling and conducting a comprehensive analysis of the existing approaches, focusing on developments from 2014 to 2023. With the help of a systematic literature review, we want to provide designers and developers of learner models with a structured overview and simplified entrance into the topic and the field of learner models. We investigate the question: What do learner models look like and how are they filled, kept up-to-date, and used?

To this end, we analyse and classify existing approaches. Our findings provide a comprehensive and structured overview of the field of learner modelling, allowing researchers to navigate and understand the diverse approaches more easily and providing developers of learner models or adaptive systems with a practical tool to access relevant information according to their needs.