Personal Response Research Articles

OP29 Young Researcher Award: Breaking New Ground in IBD Research: Organoid Models as the Vanguard for Personalized Therapies

Abstract Background Organoid technology has emerged as a pivotal tool in IBD research, offering unparalleled insights into disease mechanisms, mechanisms-of-action, and host-microbiota interactions. Advances in these models enable the establishment of co-cultures in dynamic and IBD-specific intestinal environments. Methods Intestinal crypts from biopsies of IBD patients were isolated, adult stem cells expanded into 3D organoids and differentiated to reflect epithelial subtypes. To enable easy basolateral and apical access, organoid-derived epithelial cells were cultured in Transwells. For enhanced physiological relevance, organoids were integrated into gut-on-a-chip platforms featuring microfluidic flow and peristalsis, and prolonged co-cultures with stimuli and cell types of interest. Advanced imaging techniques, including high-resolution microscopy, were used to evaluate epithelial dynamics, barrier integrity, and inflammatory responses. These models were utilized to test therapeutic compounds and assess personalized responses based on patient-specific profiles (Figure 1). Results Organoid-based models replicated key features of IBD pathology, including inflammation-induced epithelial dysfunction and barrier loss. Imaging provided critical insights into cell-cell interactions and mucus expression under inflammatory conditions and therapeutic interventions. Barrier integrity was rigorously assessed using transepithelial electrical resistance in Transwells, confirming functional disruptions associated with inflammation or ER-stress. Transcriptomic analyses revealed patient- and disease-specific responses to cytokines, microbial metabolites, and environmental stressors, demonstrating the relevance for personalized therapeutic strategies. Early gut-on-a-chip experiments validated the integration of intact (Papp) organoid-derived epithelial monolayers with stromal components and dynamic flow. Conclusion The convergence of organoid technology and gut-on-a-chip systems heralds a new era in IBD translational research. These platforms provide complementary strengths, enabling researchers to tailor models to specific investigative needs. Gut-on-a-chip models integrate organoid-derived epithelial layers with stromal, vascular, and/or immune components under dynamic flow conditions, offering unparalleled insight into the spatial and functional complexities of the gut environment. Together, these systems support personalized exploration of disease pathways and (preclinical) therapeutic efficacy, bridging the gap between in vitro modeling and clinical application. This convergence establishes a versatile foundation for precision medicine, addressing individual variability and driving transformative advances in patient care.

P0054 Human colonoid-derived monolayers and Caco-2: comparative analysis of inflammatory responses

Abstract Background Given the increasing incidence of inflammatory bowel diseases, there is a growing need for reliable preclinical models that replicate the inflamed intestinal epithelium. Immortalized Caco-2 cells are widely considered the standard in vitro model for intestinal studies, but human-derived colonoid monolayers (CDMs) could be proposed as a powerful alternative to more accurately mimic the pathophysiological features of intestinal inflammation. The present study aims to compare the functional and morphological responses induced by inflammatory cytokines TNFα and IFNγ on CDMs and Caco-2. Methods Human colonic crypts from biopsies of 5 healthy volunteers were embedded in Matrigel® and cultured for 5-7 days. IntestiCult™ OGM was used to expand cystic-like colonoids, and single cells were seeded onto coated 24-well inserts. Upon reaching confluence, the medium was switched to IntestiCult™ ODM. Caco-2 cells were cultured for 21 days on 24-well inserts. Differentiated CDMs and Caco-2 monolayers were exposed to 25 ng/mL TNFα-IFNγ for 24 hours. Cell differentiation was assessed by immunocytochemistry (ICC), and barrier integrity was evaluated by transepithelial electrical resistance (TEER) measurement. Cytotoxicity was assessed through the LDH assay. IL-8 and CCL20 release were quantified. Results Both models showed increasing TEER over the growth period, indicating the gradual development of epithelial barriers, up to 484±58 Ω*cm2 for CDMs and 700±184 Ω*cm2 for Caco-2. ICC staining for epithelial markers villin and MUC2 confirmed polarization and differentiation under basal conditions in both models, cytokines reduced the expression of epithelial markers. Inflammation reduced the barrier integrity of the monolayers (approximately 18%), although more uniformly in Caco-2 than in CDMs. IL-8 and CCL20 release was augmented by cytokines in both models, (for IL-8 of about 20 times in CDM and 2 times in Caco-2). As regards cell viability, CDMs (40% cytotoxicity) showed higher sensitivity to cytokines than Caco-2 (22% cytotoxicity). Conclusion Exposure to an inflammatory environment altered cell morphology and impaired epithelial barrier integrity in both CDMs and Caco-2 monolayers. These findings highlight the potential of CDMs as an advanced model for studying personalized responses in intestinal inflammation.

“Is It Roast Lamb for Dinner?” Reflections on Love, Loss and a Living God

This essay is a personal response to the questions that arose to confront my Christian faith as I attempted to navigate a way forward amidst the impact and consequences of a cancer diagnosis for, firstly, my sister and then my husband. It is a navigation that is still, and in some respects will always be, ongoing, but in reflecting on how the ‘wrong’ questions might be replaced by the ‘right’ ones, this essay seeks to retain hope as a guiding principle to light the way ahead.

Psychological and Behavioral Insights From Social Media Users: Natural Language Processing-Based Quantitative Study on Mental Well-Being.

Depression significantly impacts an individual's thoughts, emotions, behaviors, and moods; this prevalent mental health condition affects millions globally. Traditional approaches to detecting and treating depression rely on questionnaires and personal interviews, which can be time consuming and potentially inefficient. As social media has permanently shifted the pattern of our daily communications, social media postings can offer new perspectives in understanding mental illness in individuals because they provide an unbiased exploration of their language use and behavioral patterns. This study aimed to develop and evaluate a methodological language framework that integrates psychological patterns, contextual information, and social interactions using natural language processing and machine learning techniques. The goal was to enhance intelligent decision-making for detecting depression at the user level. We extracted language patterns via natural language processing approaches that facilitate understanding contextual and psychological factors, such as affective patterns and personality traits linked with depression. Then, we extracted social interaction influence features. The resultant social interaction influence that users have within their online social group is derived based on users' emotions, psychological states, and context of communication extracted from status updates and the social network structure. We empirically evaluated the effectiveness of our framework by applying machine learning models to detect depression, reporting accuracy, recall, precision, and F1-score using social media status updates from 1047 users along with their associated depression diagnosis questionnaire scores. These datasets also include user postings, network connections, and personality responses. The proposed framework demonstrates accurate and effective detection of depression, improving performance compared to traditional baselines with an average improvement of 6% in accuracy and 10% in F1-score. It also shows competitive performance relative to state-of-the-art models. The inclusion of social interaction features demonstrates strong performance. By using all influence features (affective influence features, contextual influence features, and personality influence features), the model achieved an accuracy of 77% and a precision of 80%. Using affective features and affective influence features also showed strong performance, achieving 81% precision and an F1-score of 79%. The developed framework offers practical applications, such as accelerating hospital diagnoses, improving prediction accuracy, facilitating timely referrals, and providing actionable insights for early interventions in mental health treatment plans.

Enhancing Individual Self-Efficacy Through a Self-Growing Memory Artificial Intelligence Agent Integrated with a Diary Application

This paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5).

Combining T cell receptor sequencing and transcriptomics to characterize tissue-resident T cells from human gut biopsies.

Gastrointestinal T cells (GI-T) play a critical role in mucosal immunity, balancing tolerance and pathogen defence. T cells recognize antigens via T cell receptors (TCRs). Next-generation sequencing (NGS) is utilized to analyse TCR repertoires in contexts such as health, haematological diseases, autoimmunity, and inflammation. While some studies have explored T cell involvement in GI conditions, the integration of different techniques and examination of diverse tissues remain underdeveloped. In our "proof of concept" study, for the first time, we combined flow cytometry, TCR sequencing and transcriptomics to analyse T cell repertoires from bulk sorted T cells and from single cells. This combination provides information about both, specificity and functionality of particular T cells. We focused on biopsy samples from the stomach, colon, and compared these to blood samples from patients with and without inflammation associated with erosive gastritis. This combined approach allows unique insights into T cell biology. Through TCR clonotype analysis, we identified oligoclonal expansion in inflamed biopsies, with minimal TCR clonotype overlap between individuals, highlighting personalized immune responses. Gene expression analysis revealed upregulation of T cell activation and signalling and chemokines in inflamed biopsies. Single-cell sequencing provided deeper insights into specific T cell populations, identifying dominant clonotypes with cytotoxic function. Our findings underscore the importance of studying affected sites to fully understand T cell responses and localized immune reactions. Our approach opens unique possibilities for studying TCR and gene expression from limited biopsy material, potentially leading to personalized therapies and biomarkers for gastrointestinal diseases.

Enhancing Consumer Agent Modeling Through Openness-Based Consumer Traits and Inverse Clustering

This study investigates the relationship between consumer personality traits, specifically openness, and responses to product designs. Consumers are categorized based on their levels of openness, and their affective responses to nine vase designs, varying in curvature and line quantity, are evaluated. The study then introduces the inverse clustering approach, which prioritizes maximizing predictive model accuracy over within-cluster similarity. This method iteratively refines cluster assignments to optimize prediction performance, minimizing errors in forecasting consumer design preferences. The results demonstrate that the inverse clustering approach yields more effective clusters than personality-based clustering. Moreover, while there is some overlap between personality-based and accuracy-based clustering, the inverse clustering method captures additional individual characteristics, extending beyond personality traits and improving the understanding of consumer product design response. The practical implications of this study are significant for product designers, as it enables the development of more personalized designs and optimization of product features to enhance specific consumer perceptions, such as robustness or esthetic appeal.

AI Chatbots and Cognitive Control: Enhancing Executive Functions Through Chatbot Interactions: A Systematic Review.

Background/Objectives: The evolution of digital technology enhances the broadening of a person's intellectual growth. Research points out that implementing innovative applications of the digital world improves human social, cognitive, and metacognitive behavior. Artificial intelligence chatbots are yet another innovative human-made construct. These are forms of software that simulate human conversation, understand and process user input, and provide personalized responses. Executive function includes a set of higher mental processes necessary for formulating, planning, and achieving a goal. The present study aims to investigate executive function reinforcement through artificial intelligence chatbots, outlining potentials, limitations, and future research suggestions. Specifically, the study examined three research questions: the use of conversational chatbots in executive functioning training, their impact on executive-cognitive skills, and the duration of any improvements. Methods: The assessment of the existing literature was implemented using the systematic review method, according to the PRISMA 2020 Principles. The avalanche search method was employed to conduct a source search in the following databases: Scopus, Web of Science, PubMed, and complementary Google Scholar. This systematic review included studies from 2021 to the present using experimental, observational, or mixed methods. It included studies using AI-based chatbots or conversationalists to support executive functions, such as anxiety, stress, depression, memory, attention, cognitive load, and behavioral changes. In addition, this study included general populations with specific neurological conditions, all peer-reviewed, written in English, and with full-text access. However, the study excluded studies before 2021, the literature reviews, systematic reviews, non-AI-based chatbots or conversationalists, studies not targeting the range of executive skills and abilities, studies not written in English, and studies without open access. The criteria aligned with the study objectives, ensuring a focus on AI chatbots and the impact of conversational agents on executive function. The initial collection totaled n = 115 articles; however, the eligibility requirements led to the final selection of n = 10 studies. Results: The findings of the studies suggested positive effects of using AI chatbots to enhance and improve executive skills. Although, several limitations were identified, making it still difficult to generalize and reproduce their effects. Conclusions: AI chatbots are an innovative artificial intelligence tool that can function as a digital assistant for learning and expanding executive skills, contributing to the cognitive, metacognitive, and social development of the individual. However, its use in executive skills training is at a primary stage. The findings highlighted the need for a unified framework for reference and future studies, better study designs, diverse populations, larger sample sizes of participants, and longitudinal studies that observe the long-term effects of their use.

Where do we go from here? The personal impact of adverse events and complications in surgeons: Results from a cross-sectional study.

Medical error is an inevitable part of modern healthcare, with a potential harmful impact for the patient and the surgeon. The phenomenon of negative personal responses for healthcare workers in the aftermath of adverse events is described as the "second casualty" and can involve significant physical and psychological distress. We present results from a cross-sectional survey of surgeons and their experience in the aftermath of adverse events or complications. Following ethical approval an anonymous online survey was distributed to all training and consultant surgeons in the Republic of Ireland. Data collected included basic demographics, personal response to an adverse event, coping strategies, contributing factors and perception of potential support structures. A total of 110 responses were received representing surgeons with a broad range of experience and specialty. Eighty-nine percent (n=98) of surgeons described negative psychological symptoms in the aftermath of adverse events. The predominant responses were anxiety, guilt and low mood. Sixty-seven percent (n=74) reported that their training did not prepare them for the personal impact of events but found informal peer and family support to be beneficial and were open to the implementation of formal support structures. Surgeons suffer in the aftermath of negative outcomes and feel unprepared to manage the personal consequences. Institutions and representative bodies must step forward to change culture and develop supports for surgeons in the aftermath of adverse events and complications.

Early assessment of IL8 and PD1+ Treg predicts response and guides treatment monitoring in cemiplimab-treated cutaneous squamous cell carcinoma

PurposeAnti-programmed cell death 1 (PD1) is the first-choice treatment in patients with advanced cutaneous squamous cell carcinoma (cSCC), when curative options are unavailable. However, reliable biomarkers for patient selection are...

John Calvin’s Theological Reflection on the Necessity of Prayer: How to Promote a Covenantal Relationship of God and Humanity in Christ

This article explores the perceived tension in John Calvin’s teachings regarding human prayer within the broader theological context of predestination and the bondage of the human will to sin. Contrary to claims of a logical contradiction, I argue that Calvin’s doctrine of prayer aligns with his soteriology, serving as a practical expression of Christian faith. Calvin places prayer within his theological anthropology, emphasizing the renewed human nature post-salvation, empowered by the Holy Spirit. Framed as a dialogue with God, prayer becomes a believer’s responsibility involving discernment, gratitude, and petitionary prayers—a simultaneous response to requests and a discipline of faith. Calvin examines the harmonious interplay of divine and human will in Christ’s prayer for believers, justifying and sanctifying imperfect human petitions through Christ’s mediation. His theological reflection suggests God’s personal response to His children’s requests, elevating them to covenantal partners in realizing his eternal salvific will in Christ.

AI-Powered GPT-Based University-Specific Chat Assistant: UniROBO

AI-powered chat applications are innovative solutions that facilitate user interaction and information access. These applications improve user experience by providing personalized and context-sensitive responses thanks to large language models and natural language processing techniques. This study examined the design and development process of UniRobo, an AI-powered chat application developed for Malatya Turgut Ozal University students and staff. UniRobo is an application that provides instant information on topics such as education, food menus, and campus events and offers personalized responses using large language models and natural language processing techniques. In the development process, based on user needs analysis, the mobile application was created with React Native, the back-end was created with Python and FastAPI, and the MongoDB database was integrated. Artificial intelligence capabilities were supported by OpenAI API and fine-tuning, thus adapting to university-specific content. Retrieval-Augmented Generation (RAG) architecture and Azure AI Search technology increased user satisfaction by providing more accurate and faster responses. As a result, UniRobo has made university life more accessible, providing users with access to fast and accurate information, and has demonstrated the potential of artificial intelligence-based solutions in the education sector.

MedInsight : A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models

Providing contextual and comprehensive medical information tailored to individual patients is critical for enabling effective care in the healthcare domain. However, existing approaches often struggle to deliver personalized responses due to the distributed nature of medical data across multiple sources such as patient records, medical literature, and online resources. To address this challenge, we present MedInsight , a multi-source context augmentation framework that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to augment patient-specific information from medical transcripts with trusted knowledge from textbooks and web resources to generate personalized and contextually relevant responses. Our framework consists of three phases: patient context retrieval, medical knowledge retrieval, and response generation. By augmenting patient context with relevant external knowledge, MedInsight generates contextually relevant responses, empowering patients and caregivers with actionable insights. Experiments on the MTSamples dataset validate MedInsight ’s effectiveness in generating contextually appropriate medical responses, using a comprehensive set of metrics including RAGAs, TruLens, ROUGE, and BertScore. Additionally, qualitative evaluations by Subject-Matter Experts (SMEs) further confirm the relevance and factual correctness of the generated responses.

Teachers at Work During the Pandemic. A Qualitative Research about Transformative Experiences and Resilience at School

The emergency due to the pandemic of COVID-19 has required great adaptability and resilience from teachers and students. This study explores teachers’ experiences with distance learning during the first Italian lockdown, investigating their personal and professional responsiveness to unexpected circumstances. A sample of 32 primary and 24 secondary school teachers responded to a narrative survey created ad hoc to highlight the differences between the experience factors that characterized the impact and the resilience in the state of emergency. Narratives were explored through T-Lab software, aiming to extract the main themes and their significance. Content analysis of teachers’ narratives indicated significant differences between the affective resources invested in primary and secondary teachers. If primary teachers were supported by affective relationships with students in handling their personal and professional resilience, secondary teachers based their management of emergency on the continuity of teaching methods and scholastic tasks.

Molecular profiling in MPN: who should have it and why?

Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are a group of blood cancers that result from somatic mutations in hematopoietic stem cells, causing constitutive activation of JAK-STAT signaling pathways with consequent overproduction of 1 or more myeloid lineages. The initiating event in MPN pathogenesis is a genetic mutation, and consequently molecular profiling is central to the diagnosis, risk stratification, and, increasingly, monitoring of therapy response in persons with MPN. In this review we summarize current approaches to molecular profiling of classical MPNs (essential thrombocythemia, polycythemia vera, and myelofibrosis), using illustrative clinical case histories to demonstrate how genetic analysis is already fully integrated into MPN diagnostic classification and prognostic risk stratification. Molecular profiling can also be used in MPN to measure response to therapy both in clinical trials and increasingly in routine clinical practice. Taking a forward look, we discuss how molecular profiling in MPN might be used in the future to select specific molecularly targeted therapies and the role of additional genetic methodologies beyond mutation analysis.

Elevated IL-10 serum levels are associated with slower serological response following syphilis treatment during pregnancy.

Approximately 50% of pregnant individuals treated for syphilis do not achieve a decline in non-treponemal titers by delivery. The serological response in pregnant persons is significantly slower compared to non-pregnant individuals, with unclear pathogenesis and clinical significance. This study aimed to determine the association between the host immune response and serological outcome in pregnant individuals with syphilis. Twenty-four females with early syphilis, including 14 pregnant individuals, were included. Pro-inflammatory and regulatory cytokines (IFN-γ, TNF-α, IL-4, IL-1β, IL-10, TGF-β) were measured before treatment and 6 months after penicillin injection. The median time to serological cure was 5months for pregnant individuals and 2months for non-pregnant females. Pregnant individuals had significantly higher serum levels of IL-10 and TGF-β at baseline and at 6months post-treatment compared to non-pregnant individuals (p < .05). A robust regulatory immune response to syphilis may be associated with a slower serological response to therapy during pregnancy.

Analysis of Knowledge, Attitude, and Practice of Self-Medication Among Students and Staff of a Caribbean Medical School.

Background Self-medication is commonly practiced, especially among medical students, administrative staff, and faculty from preclinical and paraclinical departments, driven by accessibility, familiarity with medications, and perceived convenience. This study explored the incidence, patterns, and factors influencing self-medication within the Xavier University School of Medicine, Aruba, with a primary focus on medical students and administrative staff. The faculty included in the study were from preclinical and paraclinical departments such as anatomy, physiology, biochemistry, pathology, forensic medicine, microbiology, and community medicine. Methods A descriptive, cross-sectional study was conducted among medical students, administrative staff, and faculty from preclinical departments. Data were gathered through a pre-designed, standardized, and anonymous electronic questionnaire in English, which included both open-ended questions for qualitative insights and close-ended questions for structured quantitative analysis. Open-ended questions allowed participants to provide detailed, personalized responses, offering qualitative insights into their self-medication practices and related experiences. Closed-ended questions, on the other hand, provided structured answer choices, facilitating the collection of quantitative data for statistical analysis. This combination enabled a thorough exploration of demographic details, self-medication behaviors, and the factors influencing these practices. Results Allopathic medicines were the most frequently used, reported by 34 respondents (63%), primarily sourced from pharmacies by 43 respondents (79.6%). These medicines were commonly administered directly from bottles, spoons, or measuring devices, as reported by 37 respondents (68.5%). Saving time was the primary reason for self-medication, cited by 18 respondents (33.3%), followed by lack of insurance or prior familiarity with the medication, noted by nine respondents (16.7%). Adverse effects were reported by seven respondents (13%), with drowsiness and stomach pain being the most frequent issues. Risks such as delayed diagnosis and drug resistance were also mentioned. Despite these concerns, 40 respondents (74.1%) acknowledged that self-medication could be hazardous, yet 44 respondents (81.5%) continued the practice. Additionally, 13 respondents (24.1%) felt adequately educated to self-medicate, and 37 respondents (68.5%) supported incorporating self-medication education into formal medical curricula. Conclusion Self-medication was widespread among medical students, administrative staff, and preclinical and paraclinical faculty, driven by convenience and accessibility but accompanied by risks of adverse effects and delayed diagnosis. Integrating self-medication education into medical curricula and raising awareness can foster safer healthcare behaviors and improve outcomes.

The ubiquity of countertransferential and other personal responses in progression deliberations.

Countertransferential and other types of personal responses to candidates are inevitable among psychoanalytic educators, whether they be supervisors, progression advisors, the progression committee as a whole, or course instructors. Nonetheless many educators remain unaware of these phenomena - despite the robust literature on this subject in supervisors - perhaps because we do not like to think of ourselves as having potentially deleterious personal reactions to younger colleagues with whom we have significant and highly valued relationships. This paper emphasizes the role of such phenomena in progression advisors' deliberations about candidates' development, as well as in the committee as a whole - a subject not addressed in the literature. Especially when unrecognized such responses can compromise the checks and balances function of the progression committee and significantly diminish rich and effective dialogue with candidates about their development. Examples of the potential impact and more or less effective mitigation of such responses are presented. Ensuring that faculty members and candidates alike are familiar with the inevitability of these reactions, their multiple sources and manifestations in those involved in the work of progression enhances the possibility of developing strategies to address these reactions and reduce their effect.

Aurel_AI: Automating an Institutional Help Desk Using an LLM Chatbot

The Aurel_AI research project was born from the need to implement a virtual help desk for a university, providing accurate organizational information to both internal and external clients. The information includes details about academic programs, regulations, processes, and personnel. Aurel_AI is part of a broader research program on the use of AI in academia. Traditional solutions for a help desk, such as telephone call centers, present quality and efficiency issues that are difficult to solve. Call center staff generally lack comprehensive knowledge about the institution, rely on specific information that is sometimes outdated, require additional systems for information retrieval, and experience high turnover rates. This leads to associated costs and issues related with outdated information, resulting in inaccurate responses and long waiting times. Generative artificial intelligence models, known as Large Language Models (LLMs), offer an interesting alternative for an automated virtual help desk. These models can understand even vague and poorly structured questions and generate reasonably appropriate answers. However, they are not without flaws, as they tend to present issues like "hallucinations" when the required information is not present in their training data. To minimize this problem, it is crucial to ensure that the model has precise and comprehensive information, which needs a specific methodology for information collection, validation, and updating. Base models require an adaptation process to be used for specific cases, for which techniques like Fine-Tuning and Retrieval Augmented Generation (RAG) exist. Fine-tuning retrains a model's weights with new specific information, while RAG uses both proprietary information—in this case, from the university—and publicly available internet data. Both techniques have pros and cons that need to be evaluated to select the most suitable option. They also demand appropriate and specialized infrastructure, which is often expensive. Thus, another challenge is to find a balance between suitable equipment and reasonable costs. The final system, from the user's perspective, must be accurate, flexible, and adaptable to deliver a satisfactory experience. As the results show, Aurel_AI represents an advance in the digitalization of educational services, standing out for its ability to generate accurate and personalized responses. However, its current limitations, such as handling concurrent queries and hallucinations, underscore the need for adjustments to both infrastructure and data processing methodology. With strategic improvements, the system has the potential to consolidate itself as a replicable model for multiple university digital services.

Personalized Chatbot Responses using Reinforcement Learning and User Modeling

The research focuses on chatbot interaction enrichment using reinforcement learning and user modeling. It aims to develop a personalized RL-based response generation framework for the optimization of satisfaction, engagement, and completion rates for the users. Anonymized historical interaction data from 500 users were collected to generalize user-profiles and contextual models. This was done by extracting features and observing and evaluating the control and experimental groups to test the efficacy of the working personalized system. Results indicated an overall increase of 35 percent in user satisfaction, 50 percent in session duration, and 25 percent in rates of completion of tasks against the traditional rule-based system. The results are very much in line with current literature on the improvements personalization can bring to the user's experience across many domains. The results from this study thus propose that personal AI systems powered with fine-grained models of users and reinforcement learning could obtain more engaging and efficient user interactions. The result has further-reaching implications for, for example, e-learning, customer service, and healthcare applications.