Introduction. Besides being a place for lifting weights, the gym is an interesting environment to explore how certain individuals performing routine activities engage with information. This paper investigated Cordilleran gym users’ interaction with information sources in pursuit of their fitness goals. Method. Applying sense-making approach, the research conducted interviews with forty-one regular gym users (students, athletes, trainers, and fitness enthusiasts). Analysis. Qualitative data analysis was carried out, specifically using thematic analysis, assisted by Qualitative Data Analysis (QDA) Miner Lite. Results. Gym users desire physique enhancement, weight management, overall well-being, sports training, and recreation. The identified information gaps concerned gym rules, workout techniques, nutrition, and muscle development, which were obtained from trainers, peers, and online communities. Information gathered is validated through personal experimentation, observation of trainers' physical fitness, and engagement levels of online influencers. Conclusion. The body plays an important role in information seeking, acting both as a source and a validation tool. The ideal Cordilleran physique drives many individuals to join the gym to feel a sense of belonging in the community. The gym was found to be a rich information landscape; a place where social groups with different interests, lived experiences, and cultural backgrounds engage with everyday information through group and individual activities and practices.
Introduction. As healthcare systems undergo rapid digital transformation, the usability of clinical and patient-facing technologies has become critical to adoption, user satisfaction, and patient safety. Traditional usability evaluation methods are limited in capturing the real-time cognitive, emotional, and behavioural complexities of interaction in high-stakes environments. Method. This study presents a transdisciplinary evaluation of an AI-augmented usability framework for healthcare interfaces. The framework integrates methodologies from artificial intelligence, cognitive science, usability engineering, and clinical practice, incorporating eye-tracking analytics, machine learning-based confusion prediction, and natural language processing (NLP) sentiment analysis. Forty participants, including healthcare professionals and patients, completed standardized tasks using either traditional or AI-enhanced evaluation methods. Analysis. Comparative performance across both groups was assessed using usability metrics including System Usability Scale (SUS) scores, task completion time, error rate, and task success rate. AI-based analytics—eye-tracking data, NLP sentiment scoring, and confusion prediction models—were used to generate additional insights. Statistical analysis confirmed significant differences in usability outcomes between groups. Results. The AI-enhanced evaluation significantly outperformed traditional methods in all core usability metrics. The machine learning model achieved 89% accuracy and an F1-score of 0.88. Sentiment analysis correlated strongly with self-reported satisfaction, and eye-tracking highlighted areas of cognitive friction. Conclusion. Transdisciplinary collaboration produced a novel, adaptive usability evaluation framework that offers real-time, emotionally aware insights. This replicable model enhances user-centred design and supports safer, more effective digital health technologies.
Introduction. As artificial intelligence (AI) reshapes global education and information systems, Africa’s readiness remains uneven. This study examines AI-literacy among Library and Information Science (LIS) students—future librarians—across five African countries: Ghana, Nigeria, Rwanda, South Africa and Uganda. It assesses their technical proficiency, tool usage and ethical awareness in an AI-driven era. Method and Analysis. A quantitative survey was conducted with 437 LIS students from five African countries. The Games-Howell Post Hoc Test was used to compare AI literacy across countries. ANOVA and Multinomial Logistic Regression (MLR) were applied to analyse variations in AI literacy levels based on country, study level, gender and English proficiency. Results. The findings reveal significant disparities in AI literacy between the five African nations. The results of the Games-Howell post hoc test show that both Rwanda and Uganda have significantly higher mean AI literacy scores compared to Ghana and Nigeria. In contrast, South Africa's scores did not show a statistically significant difference from the other countries, highlighting the varied readiness levels. The study found that ChatGPT dominates usage at 71.9%, while specialised tools such as Elicit remain significantly underutilised at just 4.6%. Ethical concerns among students are also notable, with 39.6% citing worries about privacy risks and 33.2% being troubled about algorithmic bias, highlighting AI anxieties and its societal impact. Conclusion. This study calls for integrating AI literacy into LIS curricula and advocating for multilingual AI tools. Where new modules are not feasible, institutions can leverage existing courses such as Information Retrieval, Digital Libraries and Research Methods to embed AI content. The results emphasise that context-driven interventions are essential to ensure AI enhances equitable knowledge access rather than deepening existing disparities.
Introduction. The adoption of generative AI raises ethical concerns for information professionals whose intermediary role requires them to ensure information quality standards that users cannot independently verify. Professional ethics address quality superficially, and the ethical implications of generative AI have not been examined from the perspective of professional obligations. Method. A scoping review assessed 644 records across three search strands, retaining 120 papers, which were analysed against ten information quality criteria and assessed for the strength of their ethical framing. Analysis. Ten practice-derived quality criteria, corresponding closely to ISO/IEC 25012, are shown to be based on a Kantian deontological framework complemented by a Marxist treatment of algorithmic surplus value. For each criterion the literature documenting the relevant AI failure mode is reviewed and its ethical treatment assessed. Results. Four criteria, accuracy, objectivity, identifiable origin, and accessibility, attract substantial ethical attention. For the remaining six, ethical treatment is limited or absent. Conclusions. The information professional's intermediary role is an ethical necessity. Professional organisations are called upon to lobby actively for AI governance using the criteria framework developed here.
Illingworth, S. (2026). Slow AI: knowing when to use AI and when to leave it alone. 99pp. ISBN: 979-8186576105
