General Knowledge Research Articles

Estrategia Pedagógica para el Desarrollo de la Educación para la Paz con Enfoque de Convivencia Pacífica, en la Básica Secundaria

This article presents the findings of a doctoral research study entitled “Peace Education with a Peaceful Coexistence Approach in Lower Secondary Education.” The main objective was to design a transformative pedagogical strategy aimed at fostering a culture of peace through a peaceful coexistence approach within lower secondary education. The study was grounded in a qualitative research paradigm and followed the principles of participatory action research, emphasizing the generation of situated knowledge with formative impact. Data collection techniques included surveys and in-depth interviews, the latter conducted through focus groups. The instruments applied to key informants consisted of a Likert-type questionnaire and a semi-structured in-depth interview guide. Data were systematized, coded, and analyzed using a methodological triangulation approach: initially through descriptive statistics—measures of central tendency, graphical representations, and percentage-based analysis—and subsequently through qualitative analysis of the focus group discussions. This comprehensive process enabled the identification of convergent and divergent criteria, the definition of analytical categories, and the emergence of new categories related to the central issue. These insights informed the construction of an alternative pedagogical strategy aligned with the principles of peace education.

Oncologists’ knowledge, practices and ethical opinions about therapeutic misconception: a French national survey

BackgroundTherapeutic misconception (TM) among research participants refers to the conflation of research goals (generating generalisable knowledge) with clinical care goals (making the best decisions for the participants). Considering the high volume of oncology research, oncologists frequently encounter TM.AimTo evaluate the knowledge, practices, and ethical concerns of French oncologists regarding TM.Materials and methodsA questionnaire was developed to assess oncologists’ knowledge and practices concerning TM, then utilised in a national survey of French oncologists from 1 June to 14 July 2023. A descriptive statistical analysis of the responses (according to a Likert scale) was carried out.ResultsIn total, 288 oncologists from various specialties responded to the survey. Initial knowledge of TM was low (16%), but after the definition was provided, 84% reported having encountered TM. Respondents indicated that they paid attention to the information given during participant inclusion; however, approximately half (46%) actively investigated the presence of TM, and 22% admitted to having encouraged TM at least occasionally. Attention to TM significantly declined over the course of study protocols. Awareness of TM, along with ethics education or participation in a research ethics committee, were identified as significant factors influencing responses. The acceptability of TM was nuanced, particularly in protocols recommended to patients receiving last-line treatments. Although 64% of respondents acknowledged a link between TM and dual roles as both investigator and physician, 78% opposed transferring investigative responsibilities to a non-referent oncologist.ConclusionTM is a widespread but still mostly unknown phenomenon which could easily be tackled for better outcomes for patients. This study revealed considerable variability in knowledge, practices, and ethical considerations related to TM among French oncologists. Enhanced education and ethical support are needed to improve awareness and foster appropriate behaviours concerning TM.Clinical trial numberNot applicable.

Abstract A006: Data Curation and Knowledge Integration Pipeline for Biomarker Discovery

Abstract Large-scale genome sequencing data from The Cancer Genome Atlas Program (TCGA) and the International Cancer Genome Consortium (ICGC) provided rich and comprehensive catalogues of many cancer types/subtypes for a large number of donors across many different cancer types. Manually curated knowledge from databases such as Reactome (https://reactome.org), OncoKB (https://www.oncokb.org), and many others, provided complementary information on pathways, clinical relevance, and therapeutic targets that are invaluable for mining biomarkers and druggable targets. One of the challenges is to harmonize all of the data so they can be uniformly processed to identify and rank potential targets. We obtained and preprocessed data (mRNA, mutations, CNVs, protein abundance, etc.) for all 32 cancer types. We also developed an internal data portal with a web interface using the Overture portal UI (https://www.overture.bio/) that allows users to efficiently interact with our preprocessed data. We are also implementing machine learning models to classify and rank potential targets for each of the available cancer types. Our machine learning models will include predictors such as differential expression between tumor and normal, tissue specificity, co-expressions with known oncogenes/tumor suppressors, copy number variations and mutation rates. Potential predictors will be evaluated and the most relevant will be included in the final models. Using known positive and negative test cases from literature, we will evaluate, optimize, and determine the best and most relevant predictors and machine learning models to use. The selected models will then be used to prioritize and rank targets by their biomarker potential. We will manually curate our results and select candidates for validation. This project provides a harmonized data structure and ML models for searching and ranking potential biomarkers and targets in an efficient and automated way. In addition, our data portal and preprocessed data allow more efficient sharing of data and improved data accessibility and reproducibility. The use of machine learning models using the preprocessed data, combined with the data portal, additional data and literature, can result in the generation of new knowledge and advances in drug discovery. Citation Format: Samantha Majoros, Mitchell Shiell, Joe Wang, Justin Richardsson, Quang M. Trinh, Richard Marcellus, David Uehling, Rima Al-awar, Shraddha Pai, Melanie Courtot, Lincoln Stein. Data Curation and Knowledge Integration Pipeline for Biomarker Discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A006.

Economic and Social Drivers of Scientific Productivity in Latin America: Evidence from a Panel Quantile Regression

The scientific production of a country drives economic, technological, and social development by generating knowledge, fostering innovation, and enhancing competitiveness. It also contributes to addressing global challenges and strengthening both education systems and international cooperation. Given its impact on national sustainability and progress, this study analyzes the influence of key variables-globalization, civil liberties, R&D expenditure, and GDP per capita-on the volume of scientific publications across 15 Latin American countries between 2001 and 2019, using the panel quantile regression method. The findings reveal that scientific production, measured by the number of published articles, is positively influenced by GDP per capita, globalization, and civil liberties. Furthermore, the study highlights the importance of R&D expenditure, both public and private, in knowledge generation. This research suggests that policymakers should promote economic development through the region's scientific capabilities to foster sustainable development and technological progress in Latin America.

Using Natural Language Processing to Describe the Use of an Online Community for Abortion During 2022: Dynamic Topic Modeling Analysis of Reddit Posts.

Abortion access in the United States has been in a state of rapid change and increasing restriction since the Dobbs v Jackson Women's Health Organization decision from the US Supreme Court in June 2022. With further constraints on access to abortion since Dobbs, the internet and online communities are playing an increasingly important role in people's abortion trajectories. There is a need for a broader understanding of how online resources are used for abortion and how they may reflect changes in the sociopolitical and legal context of abortion access. Research using online information and leveraging methods to work efficiently with large textual datasets has the potential to accelerate knowledge generation and provide novel insights into changing abortion-related experiences following Dobbs, helping address these knowledge gaps. This project sought to use natural language processing techniques, specifically topic modeling, to explore the content of posts to 1 online community for abortion (r/abortion) in 2022 and assess how community use changed during that time. This analysis described and explored posts shared throughout 2022 and for 3 subperiods of interest: before the Dobbs leak (December 24, 2021-May 1, 2022), Dobbs leak to decision (May 2, 2022-June 23, 2022), and after the Dobbs decision (June 24, 2022-December 23, 2022). We used topic modeling to obtain descriptive topics for the year and each subperiod and then classified posts. Topics were then aggregated into conceptual groups based on a combination of quantitative and qualitative assessments. The proportion of posts classified in each conceptual group was used to assess change in community interests across the 3 study subperiods. The 7273 posts shared in r/abortion in 2022 included in our analyses were categorized into 8 conceptual groups: abortion decision-making, navigating abortion access barriers, clinical abortion care, medication abortion processes, postabortion physical experiences, potential pregnancy, and self-managed abortion processes. Posts related to navigating access barriers were most common. The proportion of posts about abortion decision-making and self-management changed significantly across study periods (P=.006 and P<.001, respectively); abortion decision-making posts were more common before the Dobbs leak, whereas those related to self-management increased following the leak and decision. This analysis provides a holistic view of r/abortion posts in 2022, highlighting the important role of online communities as abortion-supportive online resources and changing interests among posters with abortion policy changes. As policies and pathways to abortion access continue to change across the United States, approaches leveraging natural language processing with sufficiently large samples of textual data present opportunities for timely monitoring, with the potential to reflect a broad range of abortion experiences, including those of people who have limited or no interaction with clinical abortion care.

Redefining knowledge-generation-driven blockchain for healthcare use: Insights from medical institutions

This study explores the factors influencing healthcare professionals’ willingness to adopt knowledge-generation-driven Blockchain technology (KGDBT) in government healthcare facilities, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. It introduces transparency as an independent variable and examines the mediating role of knowledge generation in the relationship between transparency and healthcare professionals’ intention to adopt KGDBT. Data were collected from 322 healthcare professionals in government hospitals and analyzed using SPSS version 26 and SmartPLS version 3.9 for Partial Least Squares Structural Equation Modeling (PLS-SEM). The results strongly support the theoretical framework, demonstrating that performance expectancy, effort expectancy, social influence, facilitating conditions, and transparency significantly influence healthcare professionals’ adoption of Blockchain technology. Additionally, the study identifies knowledge generation as a critical mediating factor between transparency and behavioral intention to adopt KGDBT. This research addresses the challenges of implementing Blockchain technology in healthcare by proposing a knowledge management-oriented approach to enhance its effectiveness. It highlights the critical role of transparency in promoting technology adoption and fills a gap in the literature on Blockchain and knowledge management, particularly within the Iraqi healthcare context. This study offers new insights, contributing to a comprehensive understanding of the role of knowledge generation in Blockchain adoption.

Towards a sociology of scientific protest? Response to M. Kaiser in GAIA 34/1 (2025): Knowledge and action ‐ should scientists be responsible for both?

Matthias Kaiser, in his well elaborated and substantiated response to my 2024 discussion paper on a potentially emerging survival science ethos, highlights the central question of this debate: Should scientists directly engage in both, knowledge generation and political action? I argue that we need more debate to qualify our standpoints and that a sociology of scientific protest might inform this debate.

When travel agencies entrust AI with data: the role of knowledge-based dynamic capabilities in marketing innovation

ABSTRACT This study explores how big data-powered AI enhances knowledge-based dynamic capabilities, rational marketing decision-making, and marketing innovation in travel agencies. Grounded in knowledge-based view (KBV) and dynamic capabilities theory (DCT), it examines the impact of big data-powered AI on marketing innovation using PLS-SEM analysis on 415 Taiwanese travel agencies. Findings reveal that big data-powered AI significantly strengthens knowledge acquisition and generation capabilities and rational decision-making, while knowledge combination and marketing implementation capability moderate these relationships. This study extends AI-driven marketing research and provides important insights for theoretical and practical applications.

Transforming Knowledge and Research for Just and Sustainable Futures: Implications for Higher Education Policy and Practice

The article starts by making a case as to why it is necessary to transform knowledge and research. It is argued that transformation is essential for epistemic justice and to tackle the complex problems of unsustainable development. Higher education (HE) has a crucial role given its pivotal position in knowledge generation, circulation and governance processes. However, the HE sector must be transformed to play such a role. The article argues that the current neoliberal social imaginary based on markets and competition is antithetical to the required transformations. Instead, a new social imaginary based on Mbembe’s idea of a new planetary consciousness is suggested to provide a more robust vision to base reform. The article discusses priorities for transforming teaching, research, global and civic engagement and how universities are governed. The article concludes by arguing that transformation must be considered holistically across these areas and that governments, civil society and multilateral organizations, such as UNESCO, also have a critical role in transforming the broader knowledge ecosystem HE currently operates.

Systematic Review of Integrating Technology for Sustainable Agricultural Transitions: Ecuador, a Country with Agroecological Potential

Agroecology has traditionally been implemented using conventional methods. However, the integration of precision equipment, advanced methodologies, and digital technologies (DT) is now essential for transitioning to a more modern and efficient approach. While agroecological principles remain fundamental for planning and managing sustainable food systems by optimizing natural resources, technological tools can significantly support their implementation and adoption by farmers. This transition, however, must also consider socioeconomic factors and policy frameworks to ensure that technological advancements lead to meaningful improvements in farms and agroecosystems. Across both industrialized and emerging economies, various initiatives, such as precision agriculture, digital platforms, and e-commerce, are driving the digitalization of agroecology. These innovations offer clear benefits, including enhanced knowledge generation and direct improvements to the food supply chain; however, several barriers remain, including limited understanding of digital tools, high-energy demands, insufficient financial resources, economical constrains, weak policy support, lack of infrastructure, low digital learning by framers, etc. to facilitate the transition. This review looks for the understanding of how digitalization can align or conflict with local agroecological dynamics across distinct political frameworks and reality contexts because the information about DT adoption in agroecological practices is limited and it remains unclear if digital agriculture for scaling agroecology can considerably change power dynamics within the productive systems in regions of Europe and Latin America. In South America, among countries like Ecuador, with strong potential for agroecological development, where 60% of farms are less than 1 ha, and where farmers have expressed interest in agroecological practices, 80% have reported lacking sufficient information to make the transition to digitalization, making slow the adoption progress of these DT. While agroecology is gaining global recognition, its modernization through DT requires further research in technical, social, economic, cultural, and political dimensions to more guide the adoption of DT in agroecology with more certainty.

Ph.D. productions and employment prospects in Zimbabwe; imperative from Education 5.0 heritage-based education system.

The production of PhDs has been a topical issue for debate about the need for such a qualification as a foundation for development. Literature is awash with arguments that support the need for a Ph.D. as a development thrust in many countries. In most countries in the West and Europe, their developmental thrust has been anchored on the production and employability of Ph.D. holders. Although this has been the case elsewhere, the same may not be accurate in Africa, particularly Zimbabwe. Zimbabwe engaged in an ambitious project called Education 5.0 Heritage-Based Education System as an impetus to develop the country with the production and employability of Ph.D.s as one of its objectives. Still, the progress of such a policy remains untested and over-ambitious. Reviewing three articles from the University World News one by Maina Waruru, and Eric Fredua-Kwarteng’s two articles vis-a-vis the production and employment prospects of Ph.Ds in Zimbabwe, this paper aims to critically analyze the imperatives of Heritage-Based Education 5.0 and its impact on the production and employment prospects of Ph.Ds, in Zimbabwe. From this analysis, the article argues that although the Western Ph.D. model is not entirely suitable for Africa and Zimbabwe in particular, its principles and objectives are something Zimbabwe would have to learn from as it develops its policies. Thus, the article questions the aims of the Education 5.0 policy based on the demands and expectations of Ph.D. graduates. For a country that has gone through cycles of poverty and policy changes and has little capacity to produce the minimum number of doctorates required to influence meaningful economic development, it is difficult to justify its shift in policy from the norm as far as Ph.D. production and employment are concerned. The analysis reports several areas of shortcomings, the challenges in the production of Ph.D.s in Zimbabwe, the capacity of supervisors, knowledge generation, and the employability of Ph.D. holders in Zimbabwe.

"Pre-Train, Prompt" Framework to Boost Graph Neural Networks Performance in EEG Analysis.

Electroencephalography (EEG) is a vital non-invasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex non-Euclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a "pre-train, prompt" framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL). The framework first uses unsupervised contrastive learning to pre-train on a large-scale EEG dataset. It then transfers the generic EEG knowledge learned by the model to target EEG datasets through graph prompt learning, thereby enhancing the model's performance with a limited amount of EEG data from the target domain. We tested the framework on five EEG datasets, and the results showed that GEPL outperformed traditional fine-tuning methods in classification accuracy and area under the ROC curve (AUC). GEPL demonstrated improved generalization, robustness, and computational efficiency, thereby significantly reducing the overfitting risks associated with limited EEG data. Moreover, the model provided interpretable results, highlighting relevant brain regions during classification tasks. This research suggests that the "pre-train, prompt" paradigm is well-suited for EEG analysis and offers potential applications in other domains where data are limited.

Implementation of a Data-Driven Learning Health System in Rheumatology: A Novel Application of Dashboard Quality Reporting to Support Optimal Rheumatoid Arthritis Care

ObjectivesLearning Health Systems (LHS) leverage healthcare data to drive cycles of knowledge generation for continuous care quality improvement. In Alberta, the recent launch of a provincial health information system (Connect Care, Epic Corporation) that interfaces with analytical and reporting tools, supports the use of routinely collected regional clinical data for care quality improvement efforts. As such, this project aims to implement a provincial LHS to support optimal care for Albertans with rheumatoid arthritis (RA).MethodsThis was a multiphase project. In Phase 1, we identified a starter set of quality measures. Candidate measures were reviewed for feasibility of operationalization using structured data in Connect Care along with linkage to other administrative datasets (physician billing, pharmacy data, discharge abstract database). In Phase 2, a shortlist of eligible measures that aligned with national measurement priorities were ranked by panelists including providers, field-experts and patient partners. In Phase 3, we operationalized and reported on these measures as dashboards. To do so, a case definition of RA and a list of eligible providers were identified. These definitions were validated by chart reviews completed by 2 rheumatologists and an analyst. The resulting data were reported as dynamic and interactive dashboards on Tableau using near to real-time data from the linked datasets. Dashboards allow providers to access patient-level data on their practice to support quality improvement.ResultsFrom a shortlist of eighteen measures, 9 were prioritized by a panel of providers, experts and patient partners (n=7). We developed the following dashboards to display an initial set of measures: 1) number of RA patients followed per practice/site; 2) wait times to first rheumatology consult; 3) RA disease activity assessment (process and outcome measures); 4) gaps in care (lost to follow-up, treatment, or lab monitoring). As of 09/20/2024 there were 8059 individuals with RA under rheumatology care at 4 sites. Only 44% of 6668 encounters had a documented joint count, and 24% had a composite disease activity score calculated. Of the 1600 encounters with a composite score, 17% of patients were in low disease activity/remission. We identified 1,770 individuals with &gt;12 months between rheumatology visits. Only 24% of individuals with RA were seen by a rheumatologist within 6 weeks of referral.ConclusionUsing an LHS approach, we aim to shorten and streamline the cycles of transforming data into actionable knowledge for optimized care. We are working with our provincial partners to identify appropriate strategies to address the identified gaps.Practice Reflection Award

Kazakhstan’s Academic Tourism Research: A Bibliometric Analysis of Trends, Collaboration, and Regional Positioning (2003–2023)

Kazakhstan’s tourism sector holds significant potential but remains underdeveloped, as reflected in its 2019 Travel &amp; Tourism Competitiveness Index ranking of 80th globally. Moreover, less than 5% of the country’s research and development output is commercialized into industry, indicating a gap between knowledge generation and practical innovation. This study presents a comprehensive bibliometric analysis of academic publications on tourism in Kazakhstan, using the Scopus database as the primary data source. The analysis spans 2003–2023 and examines publication volume, document types, authorship, collaboration patterns, and citation and keyword trends. A total of 377 documents were identified, with output growing markedly after 2012 and reaching approximately 60 publications in 2023. Journal articles dominate (82.5% of documents), and Kazakhstan’s publication count ranks third among post-Soviet countries (after Russia and Ukraine) but remains far below Turkey’s output. The top contributing institutions are domestic universities (led by L.N. Gumilyov Eurasian National University with 94 publications) alongside international partners, reflecting robust collaboration (over half of prolific authors are affiliated in Kazakhstan, followed by a significant cohort from Turkey). Kazakhstan’s academic tourism research is on an upward trajectory, supported by policy reforms in higher education and science since 2011. This growing body of research is poised to inform evidence based tourism policy, foster innovation (e.g., in sustainable and cultural tourism), and raise Kazakhstan’s international academic and industry profile. The findings provide valuable benchmarks by comparing Kazakhstan’s productivity with regional peers and underscore the need to leverage academic output to support national tourism development and competitiveness.

Innovation and entrepreneurship education guidance and optimization analysis based on deep learning optimization algorithm

According to the constructivist learning theory, learners need to actively construct a knowledge system in a practical situation, and deep learning, with intelligent data analysis, pattern recognition and predictive decision-making capabilities, just builds a practical platform for students to discover market opportunities and formulate innovative solutions, which effectively promotes the development of their innovation and entrepreneurship skills. At the same time, deep learning emphasizes the understanding, utilization and generation of knowledge, and further strengthens students’ innovation and entrepreneurship literacy by cultivating human-machine collaboration, which is in line with the concept of “learning by doing” in contextual cognitive theory. In addition, deep learning has the characteristics of thinking training, knowledge transfer and application, which echoes the requirements of higher-order thinking cultivation in Bloom’s educational goal classification theory, which can effectively improve the knowledge integration, transformation and application ability of college students, and enhance the ability of knowledge creation and innovation and entrepreneurship. In the application of practical education scenarios, taking the YOLOv5s network as an example, after the introduction of the SE module and the SIoU loss function, the average accuracy (mAP) of the feature detection and entrepreneurship scene recognition tasks of innovative projects is increased to 89.74% and 89.33%, respectively. This significant performance improvement intuitively demonstrates the ability of deep learning algorithms to accurately analyze education data. Through the efficient processing of educational data such as classroom teaching videos and project display images, the system can accurately identify students’ innovative thinking performance and entrepreneurial practice scenarios, and then provide data support for teachers to optimize teaching design and reform teaching methods, tailor personalized learning paths for students, truly realize data-driven educational innovation, and promote the deep integration of innovation and entrepreneurship education theory and practice.

Redesigning Knowledge Creation Support in Online Environments: An Empirical Study on Process Structures and Adaptive Strategies

Purpose: This study aims to theorize and empirically validate a recursive and mediated support process for online knowledge creation in organizational contexts. Drawing on the IEM model—which comprises internalized expression, explicit collaboration, and mediated integration—the research challenges the conventional assumption that face-to-face interaction is a prerequisite for effective knowledge generation. Study design/methodology/approach: A large-scale nationwide survey was conducted with 2,408 business professionals in Japan. Structural equation modeling (SEM) was employed to examine the hypothesized relationships among the IEM model constructs, including a feedback loop structure from mediated integration to internalized expression. Findings: The analysis revealed that all three phases—internalized expression, explicit collaboration, and mediated integration—are not only sequentially connected but also mutually reinforcing. The recursive feedback from mediated integration to internalized expression was statistically significant, underscoring the adaptive and cyclical nature of digital knowledge creation. Originality/value: This study introduces a theoretically grounded and empirically validated IEM model for online knowledge creation. By demonstrating that effective knowledge generation can be sustained through digitally mediated, empathy-supported processes, the study provides a novel framework with strong implications for remote collaboration and knowledge management in hybrid work environments.

Labour productivity disparities in European regions: the impact of agglomeration effects

Disparities in regional productivity in Europe have increased since 2000, and the rapid progress of the richest regions is considered to be one of the main forces behind this change. In this paper, we estimate a production function, taking into account spatial dependence where labour productivity depends on regional capital intensity, knowledge-related variables (human capital and patent intensity) and the presence of agglomeration economies. The data cover a set of 121 NUTS-2 regions belonging to nine European countries over the period 2000–2014. Our econometric analysis throws up new and robust evidence pointing to the positive total impact of agglomeration on regional levels of labour productivity. We find that the positive externalities deriving from agglomeration are significant only in the region itself (direct impact), while the physical and human capital exhibit direct positive effects that are partially counterbalanced by the presence of negative spatial spillovers. But, while physical capital per worker preserves a significant positive total effect, the human capital does not. Finally, we find that knowledge generation impacts directly in local labour productivity and spread out significantly to other regions. These results are used to propose a more balanced use of regional policies to propel territorial resources in order to compensate the dominance of agglomeration economies.

Smart Data-Enabled Conservation and Knowledge Generation for Architectural Heritage System

In architectural heritage conservation, fragmented data practices and heterogeneous formats hinder knowledge extraction, limiting the translation of raw data into actionable conservation insights. This study proposes a knowledge-centric framework integrating smart data methodologies to bridge this gap. The framework synergizes Heritage Building Information Modeling (HBIM), semantic knowledge graphs, and knowledge bases, prioritizing three interconnected dimensions: geometric digitization through 3D laser scanning and parametric HBIM reconstruction, semantic enrichment of historical texts via NLP and rule-based entity extraction, and knowledge graph-driven discovery of spatiotemporal patterns using Neo4j and ontology mapping. Validated through dual case studies—the Historical Educational Sites in South China (humanistic narratives) and the Dong ethnic drum towers (structural logic)—the framework demonstrates its capacity to automate knowledge generation, converting 20.5 GB of multi-source data into 2652 RDF triples that interconnect 1701 nodes across HBIM models and archival records. By enabling real-time visualization of semantic relationships (e.g., educator networks, mortise-and-tenon typologies) through graph queries, the system enhances interdisciplinary collaboration. Furthermore, the proposed smart data framework facilitated the generation of domain-specific knowledge through systematic data valorization, yielding actionable insights for architectural conservation practice. This research redefines conservation as a knowledge-to-action paradigm, where smart data methodologies unify tangible and intangible heritage values, fostering data-driven stewardship across cultural, historical, and technical domains.

Chintan-manan: a critical reflections approach to teaching interviewing skills in an Indian context

ABSTRACT A decontextualized focus on universal variables, shaped by a positivist paradigm, continues to dominate psychological research. In contrast, training students in qualitative methods—particularly interviews—can foster sensitivity to the cultural contexts in which knowledge is produced. This paper outlines a three-phase practicum designed to develop socio-culturally grounded interviewing skills in an undergraduate class for Psychology students in India. The practicum encourages active student participation and critical reflection, challenging hierarchical structures in knowledge generation. Students progress through three stages: conducting unstructured interviews, developing interview guides, and undertaking semi-structured interviews. The concept of chintan-manan—contemplative reflection in the Indian context—was introduced to support students in engaging critically with their learning process. Throughout, they maintain detailed process reports that link theoretical learning on interviewing with practical field experiences. These process reports incorporate students’ reflections on research topic selection, interview design, ethical considerations, and field experiences. Data from these reports, along with teachers’ observational notes, were inductively analyzed. The resulting themes discuss how reflective practice, rooted in cultural traditions, can enhance students’ understanding of the ethical, methodological, and interpersonal complexities involved in qualitative research.

ARTIFICIAL INTELLIGENCE IN ACADEMIA: OPPORTUNITY OR CHALLENGE?

In recent years, the rapid advancement of Artificial Intelligence (AI) has had a profound and transformative impact across a wide range of sectors, including education and scientific research. As the primary environment for the generation, exchange, and preservation of knowledge, academia finds itself at the forefront of this technological shift. AI presents a dual reality for academic institutions: it brings forward unprecedented opportunities for innovation and efficiency, while simultaneously introducing complex ethical, pedagogical, and operational challenges. This article aims to examine the multifaceted influence of AI on higher education, particularly focusing on the evolving role of educators. While numerous studies have explored AI's applications in learning management systems, automated grading, and research data analysis, this paper places a particular emphasis on the human dimension namely, how academic staff are responding to the integration of AI tools into their teaching and research practices. Through an analytical overview, the paper identifies both the benefits and risks associated with AI implementation in academia. On the one hand, AI offers the potential to enhance personalized learning, automate repetitive administrative tasks, and improve access to educational resources. These advancements could significantly increase productivity and support more inclusive educational practices. On the other hand, the growing reliance on AI raises serious concerns related to academic integrity, data privacy, algorithmic bias, and the potential deskilling of educators. The article highlights the urgent need for clear institutional strategies that include professional development for educators, ethical guidelines for AI use, and investment in digital infrastructure. Without these measures, the integration of AI could lead to fragmentation and inequality within the academic system. Educators must not only adapt to technological innovations but also actively shape the discourse around the responsible and meaningful use of AI in education. In conclusion, while AI undoubtedly holds transformative potential for academia, its successful and ethical implementation depends on a balanced approach one that values both innovation and the enduring principles of academic freedom, critical thinking, and human-centered learning.