Open Access

Along with the increased use of automation processes in every other task, job recommendations as well as hiring have also become partially automated. In the process of applying only to those jobs that are recommended by a system or choosing only from those candidates that are selected by an automated system, it becomes highly important to find out if the automated systems are trustworthy enough to provide fair decisions. There has been research on fairness in this sector, where the main focus has been on a single protected attribute, in most cases ’gender.’ That is why the aim of this research is to delve deeper into a deep learning transformer-based algorithm used for job matching with a text-based resume dataset containing several demographic attributes to investigate the fairness of the algorithm not only for gender but also for other demographic groups such as race, age group, and experience level. The fairness evaluation has been carried out using multiple fairness metrics, including demographic parity, conditional demographic parity, and equal opportunity. The transformer models that are pretrained language models are chosen for this study due to their ability to understand the meaning and context of words in resumes and job descriptions. The thesis further investigates bias using an alternative approach by working on a dataset containing varying protected attributes and then conducting a comparable analysis of several bias mitigation methods, including multiple layers of data resampling, along with sensitivity testing through data modification in the preprocessing step of the recommendation process. The research reveals that a system performing fairly when considering a single protected attribute can even hide intersectional unfairness and that the bias mitigation methods do not ensure a balanced improvement across each subgroup when considering multiple combinational demographic groups.

Generative AI tutoring tools predominantly refine the wording, structure, and feasibility of a student’s initial problem framing. Whether AI assistance that instead poses counter-proposals – alternative framings, stakeholder perspectives, and questioned assumptions – changes the originality of the resulting framing is an open empirical question for data science education. This thesis investigates that Question in an exploratory study with thirty postgraduate students at Universität Koblenz. Each participant interacted with a bespoke web platform that walked them through three fixed-order stages on a sharedWi-Fi-dormitory dataset description: an Editorstyle AI stage, a Challenger-style AI stage, and a final unassisted synthesis. The final synthesis topic was rated on four dimensions (originality, feasibility, reasoning quality, clarity) by a human expert rater and an LLM second rater (Anthropic Claude) blinded to group assignment. Because the planned counterbalanced within-subjects crossover could not be implemented, participants were sorted post hoc into an Editor-influenced and a Challengerinfluenced group based on triangulated self-reported impact and preference. Topics from the Challenger-influenced group were rated substantially higher on originality (Cohen’s d = 3.67, 95% CI [2.50, 4.84], p < .001, ICC = 0.859); topics from the Editor-influenced group were rated higher on feasibility (d = −2.04, 95% CI [−2.92,−1.16]) and clarity (d = −1.07, 95% CI [−1.84,−0.30]); a secondary unexpected association favoured the Challenger-influenced group on reasoning Quality (d = 1.03, 95% CI [0.27, 1.79]). The latter three dimensions had inter-rater reliability below the pre-specified 0.70 threshold and their effect-size magnitudes are interpreted as exploratory estimates. Perception data and open-ended responses suggested participants treated the two AI styles as functional complements assigned to distinct task-contexts, and 94% indicated interest in an integrated switcher mode. Because the analytic groups were defined by self-report and because the stage order was fixed, these results should be read as associations rather than causal effects of prompt style. The thesis contributes (a) empirical evidence of large betweengroup differences in framing originality consistent with a counter-proposal mechanism; (b) a methodological case study of a hybrid human-LLM rating protocol with dimension-specific reliability documentation; and (c) design directions for educational AI tools that surface, rather than choose for the learner, the kind of cognitive assistance offered.

Artificial intelligence (AI) is becoming more widely used in a number of industries, including in the field of education. Applications of artificial intelligence (AI) are becoming crucial for schools and universities, whether for automated evaluation, smart educational systems, individualized learning, or staff support. ChatGPT, anAI-based chatbot, offers coherent and helpful replies based on analyzing large volumes of data. Integrating ChatGPT, a sophisticated Natural Language Processing (NLP) tool developed by OpenAI, into higher education has sparked significant interest and debate. Since the technology is already adapted by many students and teachers, this study delves into analyzing the sentiments expressed on university websites regarding ChatGPT integration into education by creating a comprehensive sentiment analysis framework using Hierarchical Residual RSigELU Attention Network (HR-RAN). The proposed framework addresses several challenges in sentiment analysis, such as capturing fine-grained sentiment nuances, including contextual information, and handling complex language expressions in university review data. The methodology involves several steps, including data collection from various educational websites, blogs, and news platforms. The data is preprocessed to handle emoticons, URLs, and tags and then, detect and remove sarcastic text using the eXtreme Learning Hyperband Network (XLHN). Sentences are then grouped based on similarity and topics are modeled using the Non-negative Term-Document Matrix Factorization (NTDMF) approach. Features, such as lexico-semantic, lexico structural, and numerical features are extracted. Dependency parsing and coreference resolution are performed to analyze grammatical structures and understand semantic relationships. Word embedding uses the Word2Vec model to capture semantic relationships between words. The preprocessed text and extracted features are inputted into the HR-RAN classifier to categorize sentiments as positive, negative, or neutral. The sentiment analysis results indicate that 74.8% of the sentiments towards ChatGPT in higher education are neutral, 21.5% are positive, and only 3.7% are negative. This suggests a predominant neutrality among users, with a significant portion expressing positive views and a very small percentage holding negative opinions. Additionally, the analysis reveals regional variations, with Canada showing the highest number of sentiments, predominantly neutral, followed by Germany, the UK, and the USA. The sentiment analysis results are evaluated based on various metrics, such as accuracy, precision, recall, F-measure, and specificity. Results indicate that the proposed framework outperforms conventional sentiment analysis models. The HR-RAN technique achieved a precision of 98.98%, recall of 99.23%, F-measure of 99.10%, accuracy of 98.88%, and specificity of 98.31%. Additionally, word clouds are generated to visually represent the most common terms within positive, neutral, and negative sentiments, providing a clear and immediate understanding of the key themes in the data. These findings can inform educators, administrators, and developers about the benefits and challenges of integrating ChatGPT into educational settings, guiding improvements in educational practices and AI tool development.