The researchers embarked on a comprehensive examination of student theses, a largely underexplored resource in sustainability research. By applying advanced NLP techniques to the text of thousands of theses, they were able to identify key themes, trends, and gaps in sustainability discourse. This method not only provided a quantitative analysis of textual data but also unveiled qualitative insights that traditional research methods often overlook. As academia continues to produce a vast amount of written material each year, the researchers argue that leveraging data-driven analytical tools like NLP can greatly enhance our understanding of how sustainability is currently conceptualized among students.

One of the most compelling findings of the study was the varying degrees of focus on sustainability across different fields of study. For instance, disciplines closely related to environmental science demonstrated a stronger emphasis on ecological sustainability, while other fields, such as business and the humanities, often approached sustainability from an ethical or socio-economic perspective. This disparity raises important questions regarding curriculum development and the need for interdisciplinary collaboration. The researchers advocate for a more integrative approach that bridges knowledge across disciplines to create a more cohesive understanding of sustainability issues.

Furthermore, the study identified a concerning trend: a significant number of student theses neglect comprehensive sustainability analysis. This gap reflects a broader challenge within higher education, where sustainability is often relegated to being a buzzword rather than a core principle. The authors suggest that it is crucial for academic institutions to foster an educational environment that encourages deep engagement with sustainability topics. This can be achieved through curriculum revisions, enhanced faculty training, and increased availability of resources focused on sustainable development.

The implications of this research extend beyond academia; they resonate with policymakers and industry leaders alike. As the global economy increasingly grapples with sustainability challenges, the education system plays a pivotal role in preparing the future workforce to innovate on these issues. The researchers highlight the importance of aligning educational outcomes with the skills and knowledge necessary for addressing sustainability crises in various sectors, suggesting that a more robust incorporation of sustainability in academic programs could facilitate this alignment.

Moreover, the study’s use of NLP as a tool for analyzing academic text opens the door for future research possibilities. By building on the methodologies derived from this study, further analyses can be conducted to explore other dimensions of academic writing on sustainability. This could include examining how sustainability rhetoric evolves over time, understanding students’ attitudes towards sustainability, or probing institutional commitment to sustainable practices based on the content of theses. In this way, NLP not only serves as a methodological tool but also as a strategic weapon in the trench warfare against climate change.

As we stand at a crucial juncture in combating environmental degradation, the role of education cannot be understated. Educational institutions must not only impart knowledge but also serve as catalysts for change by embedding sustainable practices in every aspect of their operation. This study exemplifies how scholars can critically reflect on and assess the frameworks within which education occurs. By utilizing modern technology to enhance traditional pedagogical methods, institutions can foster a new generation of thinkers and doers who prioritize sustainability in all endeavors.

In conclusion, the research conducted by Sundemo, Löfgren, and Yu serves as a wake-up call for higher education institutions around the globe. The findings underscore an urgent need for a paradigm shift in how sustainability is taught and practiced within universities. The authors challenge educators to reevaluate their approaches to curriculum design and student engagement to ensure that sustainability becomes an intrinsic component of the educational experience. In doing so, they advocate for a future where sustainability is not merely an add-on but a foundational aspect of higher education, preparing students to confront the multifaceted environmental challenges ahead.

Thus, the study does not merely contribute to academic knowledge but serves as a directive for educational reform. It invites stakeholders at all levels of academia to reconsider their roles in the sustainability conversation and to act boldly and innovatively. By mobilizing resources and intellectual capital in support of sustainability, universities can continue to lead the charge in fostering a more sustainable future for all.

The paper’s findings, methodologies, and implications create a rich tapestry of insights that can guide institutions of higher learning as they navigate the complexities of integrating sustainability into their frameworks. The synthesis of NLP with sustainability studies presents a pioneering model that other researchers can emulate. With the right tools and methodologies in place, the potential for transformative change in higher education is vast and meaningful.

Subject of Research:
Analysis of sustainability in higher education student theses using natural language processing.

Article Title:
Mapping sustainability in higher education: a natural language processing analysis of student theses.

Article References:

Sundemo, M., Löfgren, Å., Yu, Y. et al. Mapping sustainability in higher education: a natural language processing analysis of student theses.
High Educ (2025). https://doi.org/10.1007/s10734-025-01526-9

Image Credits: AI Generated

DOI:

Keywords: Sustainability, Higher Education, Natural Language Processing, Student Theses, Curriculum Development, Environmental Science.

CRISPR technology itself has already reshaped molecular biology by enabling precise genome editing, with applications ranging from treating genetic disorders to enhancing agricultural traits. Yet, despite its transformative potential, the intricacies of designing accurate, efficient CRISPR experiments remain a significant bottleneck. Researchers often grapple with protracted cycles of trial and error to optimize guide RNA designs, target selections, and off-target risk assessments. CRISPR-GPT addresses this challenge head-on by leveraging an extensive corpus of CRISPR experimental data and scientific discourse accumulated over more than a decade to provide a predictive and interactive design framework.

At the heart of CRISPR-GPT lies a sophisticated natural language processing model trained on eleven years of expert knowledge, including online expert conversations and published literature on CRISPR methodologies. This deep training enables the AI to “think” like an experienced geneticist, parsing user queries articulated in everyday language and generating comprehensive experimental plans. Users communicate their research objectives, gene sequences, and specific constraints through a text-based interface, after which CRISPR-GPT synthesizes tailored strategies for genome editing while preemptively highlighting common experimental pitfalls based on historical patterns.

One notable example illustrating CRISPR-GPT’s efficacy involved undergraduate researcher Yilong Zhou from Tsinghua University. Tasked with activating genes in melanoma cells to investigate immunotherapy resistance, Zhou was able to successfully design his CRISPR activation experiment on a single attempt, a feat that frequently requires multiple iterations even for more experienced scientists. Through an engaging dialogue with the AI, Zhou received detailed explanations at each step, which demystified complex processes and fostered a deeper conceptual understanding, effectively transforming CRISPR-GPT from a mere computational tool into an accessible and patient lab partner.

The system’s versatility is further exemplified by its three distinct operational modes—beginner, expert, and question-answer. In beginner mode, CRISPR-GPT adopts a didactic stance, providing not only procedural recommendations but also detailed reasoning behind each suggestion, making it ideal for students and early-career researchers. Expert mode positions the AI as a peer collaborator, engaging advanced practitioners without excess elaboration. The Q&A function serves as a rapid-response mechanism for addressing specific technical inquiries, streamlining dialogues between scientists and enhancing research efficiency.

CRISPR-GPT also incorporates predictive modeling of off-target editing events, a critical aspect of CRISPR experimentation. Off-target mutations can introduce unintended genetic alterations, potentially leading to erroneous conclusions or harmful side effects in therapeutic contexts. By integrating vast datasets encompassing known off-target propensities and experimental outcomes, the AI can estimate the likelihood and potential consequences of such events, enabling researchers to select guide RNAs with optimized specificity and safety profiles. This capability not only reduces the need for extensive validation rounds but also bolsters the biosecurity and ethical conduct of gene-editing research.

Safety and ethical responsibility are integral to the design of CRISPR-GPT. Recognizing the dual-use nature of gene-editing technologies, the development team embedded safeguards that detect and prevent AI assistance for unethical requests, such as attempts to engineer viruses or edit human embryos improperly. Upon encounter of such inputs, the system halts interactions and issues warnings, reflecting a proactive stance toward bioethical norms. Furthermore, Stanford’s team is collaborating with regulatory bodies, including the National Institute of Standards and Technology, to establish frameworks that ensure the technology’s deployment adheres to rigorous ethical guidelines and biosecurity standards.

The impact of CRISPR-GPT extends beyond individual labs. Because it condenses layers of accumulated expertise into a single accessible interface, it has the potential to democratize genetic engineering across universities, agricultural biotech firms, and medical research centers globally. This inclusive approach could catalyze breakthroughs in disease modeling, agricultural innovation, and personalized medicine by enabling a broader community of scientists to harness sophisticated gene-editing techniques with unprecedented ease.

Looking ahead, the developers envision expanding the CRISPR-GPT architecture into a broader suite of AI agents tailored to diverse biological tasks. Future iterations may aid in generating stem cell lines, unraveling complex molecular pathways implicated in cardiovascular disease, or automating data-intensive workflows in systems biology. This modular, agent-based approach aligns with a growing paradigm that sees artificial intelligence as an indispensable collaborator in scientific discovery, capable of tackling intricate problems through iterative learning and natural language interaction.

The framework supporting CRISPR-GPT is publicly accessible through the Agent4Genomics platform, which hosts an array of AI tools designed to aid genomic research. This openness not only fosters transparency but also invites the global scientific community to contribute data, refine algorithms, and enhance functionalities, further accelerating the pace of innovation.

CRISPR-GPT’s introduction heralds an exciting convergence of artificial intelligence and molecular genetics, where machines augment human intuition and expertise. By reducing experimental uncertainties and expediting the cyclical process of hypothesis generation, testing, and refinement, this technology holds the promise of generating lifesaving therapies in months rather than years. As genetic medicine continues to evolve at a breakneck pace, intelligent assistants such as CRISPR-GPT will undoubtedly become indispensable partners in the pursuit of understanding and manipulating the very code of life.

Subject of Research: Not applicable
Article Title: CRISPR-GPT for agentic automation of gene-editing experiments
News Publication Date: 30-Jul-2025
Web References: https://www.nature.com/articles/s41551-025-01463-z
References: Cong, Le et al., “CRISPR-GPT for agentic automation of gene-editing experiments,” Nature Biomedical Engineering, July 30, 2025.
Keywords: Artificial intelligence, CRISPRs, Genetic material, Computational simulation/modeling

The research team embarked on a comprehensive examination of 1.2 billion social media entries posted during 2019, harnessing data from two of the world’s largest platforms: Twitter and Weibo. Employing cutting-edge natural language processing techniques, specifically the Bidirectional Encoder Representations from Transformers (BERT) model, in 65 different languages, the scientists were able to quantify sentiment scores across a diverse range of cultural and linguistic contexts. Each post was rated on a scale from 0.0, indicating extremely negative emotions, to 1.0, representing highly positive expressions. This large-scale sentiment analysis was then spatially aggregated across nearly 3,000 distinct geographical locations and cross-referenced with local temperature data, enabling a robust correlation between heat extremes and shifts in expressed mood.

A striking discovery emerged when the dataset was dissected in the context of economic disparity. The analysis found that in lower-income countries, where average annual per-capita income falls below $13,845 according to World Bank standards, the negative impact of temperatures exceeding 95 degrees Fahrenheit (35 degrees Celsius) on sentiment increased by approximately 25 percent. In wealthier nations, the effect was discernibly muted, with sentiment negativity rising by around 8 percent under similar heat conditions. This disparity underscores the amplified vulnerability of economically disadvantaged populations to climate-induced psychological stressors, highlighting a critical social equity issue in climate adaptation strategies.

The study’s co-author, Professor Siqi Zheng of MIT’s Department of Urban Studies and Planning and the Center for Real Estate, emphasizes the broader implications of these findings. Zheng notes that this research “opens up a new frontier in understanding how climate stress is shaping human well-being at a planetary scale.” Unlike previous studies that narrowly focused on physical health and economic metrics, this investigation introduces emotional well-being as an integral dimension of climate impact assessment. By integrating sentiment analysis with environmental data, the work pioneers a novel interdisciplinary approach that bridges urban studies, climatology, and psychology.

Technically, the application of BERT—one of the most sophisticated transformer-based language models—enabled nuanced understanding of emotional content across a vast linguistic spectrum. BERT’s ability to grasp complex syntax and semantic subtleties allowed the researchers to accurately decode sentiments embedded in short social media posts, which traditionally pose challenges for natural language processing due to their brevity and informal structure. This methodological innovation permitted a scalable assessment that surpasses conventional survey techniques, enabling real-time, global emotional monitoring over extended periods.

Beyond immediate correlations, the research also ventured into long-term projections, leveraging global climate models to estimate how emotional well-being might evolve under continued warming trends. Assuming some degree of human adaptation to heat stress, the models forecast a 2.3 percent decline in global emotional positivity attributable solely to high-temperature days by the year 2100. Although this figure may appear modest, it represents a significant psychological burden when extrapolated across billions of individuals, with potential cascading effects on social cohesion, productivity, and mental health infrastructures worldwide.

One of the notable challenges and caveats acknowledged by the researchers pertains to the demographic representativeness of social media users. The platforms analyzed tend to underrepresent certain population segments, such as very young children and the elderly, who are simultaneously known to be disproportionately sensitive to heat-related health risks. Consequently, the study’s estimates of emotional impacts may be conservative, as these vulnerable groups could experience even more pronounced declines in well-being during extreme temperature events but are less likely to express these sentiments online.

Moreover, the study elucidates that while heat stress broadly influences sentiment, the underlying mechanisms may be multifaceted. Physiological discomfort, disrupted sleep patterns, social tensions, and economic pressures exacerbated by climatic extremes all converge to shape emotional responses. Such complexity beckons further multidisciplinary inquiry to disentangle causal pathways and identify mitigating interventions. The authors advocate for integrating emotional resilience into societal adaptation frameworks, recognizing that fostering mental robustness will be crucial alongside traditional physical and infrastructural measures.

The global scope and scale of the dataset stand out as pioneering features. This study represents one of the first attempts to harness social media at a planetary level for environmental psychology research. By analyzing sentiments across nearly 3,000 locations distributed worldwide, the researchers achieved unprecedented geographic granularity, capturing heterogenous cultural and economic contexts that color climate-induced emotional experiences. This breadth affords policymakers and scientists a more detailed understanding of where and how to target support and adaptation efforts.

The research was conducted under the aegis of MIT’s Sustainable Urbanization Lab, a hub for interdisciplinary investigations into the interactions between urban environments, climate change, and human behavior. The team’s collaborative approach brought together experts from MIT, Chinese Academy of Sciences, Harvard University, Duke University, Maastricht University, and the Laureate Institute for Brain Research. This diverse expertise ensured rigorous analysis bridging computational linguistics, urban planning, climatology, and behavioral science.

Importantly, the dataset produced by this investigation has been made publicly accessible through the Global Sentiment project, enabling scientists, urban planners, and policymakers worldwide to explore intricate connections between climate variables and human sentiment. This open resource represents a valuable step toward democratizing climate data and fostering informed, evidence-based responses to the growing emotional and social challenges posed by global warming.

As the planet continues to warm, this research underscores the necessity of broadening our understanding of climate impacts beyond the tangible to include the intangible yet profound alterations in human emotional landscapes. By illuminating the silent suffering induced by extreme heat through the lens of social media, the study calls for urgent action to incorporate psychological well-being into global climate strategies, ensuring not only survival but also the emotional health of societies in the decades ahead.

Subject of Research: The impact of rising global temperatures on human emotional well-being, analyzed through large-scale social media sentiment data.

Article Title: Rising Temperatures Are Altering Human Sentiment Globally

News Publication Date: 21-Aug-2025

Web References: https://www.globalsentiment.mit.edu/

References: DOI 10.1016/j.oneear.2025.101422 (One Earth)

Keywords: Social media, Human behavior, Climatology, Earth climate, Climate change, Climate change adaptation

The Evidence Triangulator addresses a fundamental challenge in the realm of evidence-based science: integrating diverse streams of causal evidence generated from varying research designs. Traditional methods often struggle with synthesizing findings that arise from heterogeneous methodologies, including randomized controlled trials, observational studies, and quasi-experimental designs. This tool harnesses the interpretative and generative capacities of LLMs to bridge these gaps, offering a cohesive and automated approach to evidence aggregation.

At the heart of the Evidence Triangulator is an advanced natural language processing framework fine-tuned to rigorously parse scientific literature. Unlike conventional meta-analytical tools that rely heavily on structured data and manual curation, this system can ingest unstructured textual data from a vast spectrum of publications. It then identifies causal claims, extracts relevant variables, and assesses the methodological rigor implicit in each study design. This level of understanding allows the model not only to collect evidence but to synthesize it in a meaningful and causally coherent manner.

The research team behind this innovation, led by Shi, Zhao, and Chen, designed the Evidence Triangulator to function across multiple domains of scientific inquiry. Their approach demonstrates significant versatility, reflecting the model’s ability to interpret complex causal relationships irrespective of the disciplinary context or study framework. This generalizability marks a substantial advancement over domain-specific tools, which often face limitations when transferred across fields or study designs.

One of the profound advantages of this system lies in its ability to perform “evidence triangulation,” a process where independent lines of causal evidence converge to reinforce or refute hypotheses. In practical terms, the Evidence Triangulator operationalizes this concept by integrating data from experimental, observational, and quasi-experimental studies, automatically detecting consistencies or conflicts within the body of evidence. This functionality has profound implications for improving the reliability and robustness of scientific conclusions.

Moreover, the Evidence Triangulator incorporates mechanisms to evaluate the quality and bias inherent in various study designs. Given the known limitations and potential confounders associated with observational data versus randomized trials, the model contextualizes each piece of evidence relative to study type, sample size, and other methodological considerations. This analytical depth ensures that synthesized conclusions reflect a balanced appraisal of the evidence strength and are less susceptible to misleading inferences.

Beyond its methodological sophistication, the system’s capacity for scalable evidence synthesis enables researchers to process and interpret vast quantities of scientific literature swiftly. In an era where the volume of published research grows exponentially, tools like the Evidence Triangulator serve as critical aids in distilling meaningful insights from overwhelming data. This not only accelerates research timelines but also democratizes access to synthesized knowledge, enabling broader communities to engage with cutting-edge causal science.

The impact of the Evidence Triangulator also extends to policy-making and clinical decision-making. By generating synthesized causal evidence that is both comprehensive and interpretable, the system can inform evidence-based guidelines, health policy frameworks, and strategic interventions with greater confidence. This bridges the long-standing gap between fragmented research findings and actionable insights, enhancing the translation of scientific discovery into societal benefit.

Technically, the tool leverages transformer-based architectures characteristic of contemporary LLMs but introduces novel adaptations tailored for causal inference tasks. These adaptations include supervised fine-tuning on curated datasets annotated for causal language and study design features. The training pipeline emphasizes the ability to distinguish correlation from causation within complex text, a task traditionally challenging for automated systems. Additionally, the model integrates probabilistic reasoning modules to estimate confidence in extracted causal claims.

Another salient feature is the Evidence Triangulator’s interactive interface, which allows researchers to query causal hypotheses and visualize synthesized evidence across study designs dynamically. This transparency and user-centric design support hypothesis generation, critical appraisal, and collaborative interrogation of scientific claims. Furthermore, the interface fosters reproducibility by maintaining detailed provenance records of evidence sources and synthesis pathways.

Initial validation experiments reported by the authors demonstrate impressive performance metrics, with the system achieving high precision and recall in identifying causal statements across diverse literature samples. Moreover, comparative analyses indicate that the Evidence Triangulator surpasses existing automated tools in both extraction accuracy and synthesis coherence. Importantly, expert reviewers confirmed the validity of the synthesized conclusions, underscoring the system’s practical utility.

While the Evidence Triangulator heralds a new paradigm in evidence synthesis, the authors acknowledge challenges and future directions. These include expanding the tool’s capacity to handle multilingual scientific texts, enhancing the interpretability of causal inference mechanisms, and integrating real-world evidence from clinical registries and databases. Additionally, ongoing refinement aims to mitigate any biases that may be inadvertently encoded within training data, ensuring equitable and robust evidence processing.

The advent of such intelligent systems underscores a broader trend in scientific research: the fusion of artificial intelligence with methodological rigor to tackle complex, multidisciplinary problems. By embodying principles of transparency, scalability, and methodological diversity, the Evidence Triangulator exemplifies how AI can augment human expertise rather than replace it, fostering a collaborative and nuanced approach to scientific discovery.

In conclusion, the Evidence Triangulator represents a transformative advancement that leverages the power of large language models to navigate and synthesize the intricate landscape of causal evidence across study designs. As this technology matures, it holds the potential to accelerate the pace of discovery, enhance the reliability of causal claims, and ultimately support better decision-making in science and policy. The impact of this work will likely resonate across disciplines, ushering in a new era of evidence synthesis powered by AI intelligence.

Subject of Research: Using large language models to extract and synthesize causal evidence across diverse scientific study designs.

Article Title: Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs.

Article References:
Shi, X., Zhao, W., Chen, T. et al. Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs. Nat Commun 16, 7355 (2025). https://doi.org/10.1038/s41467-025-62783-x

Image Credits: AI Generated

DECOTA operates on the principle of thematic analysis, which has long been a fundamental qualitative research technique. This technique traditionally involves labor-intensive processes where researchers meticulously categorize and interpret the nuances embedded within free-text responses. In stark contrast, DECOTA leverages advanced natural language processing and machine learning enabled by finely-tuned large language models. The consequence of this innovation is a level of efficiency previously thought unattainable in qualitative data analysis. This tool can sift through thousands of responses, distilling them into clear patterns and common themes in a matter of minutes, rather than months.

The effectiveness of DECOTA can be quantified. A comparative analysis conducted by the University of Bath research team revealed that DECOTA can analyze data approximately 380 times faster than human researchers. Additionally, the cost-effectiveness of this tool is significant, as it can process responses from 1,000 participants at a mere $0.82, whereas traditional human analysis would typically incur costs exceeding $1,500. This remarkable efficiency not only decreases financial burdens but also increases the accessibility of qualitative research across various sectors that may have previously lacked the resources to undertake such extensive analyses.

One of the defining features of DECOTA is its human-like accuracy, evidenced by a performance agreement rate of 92% with human-coded results. This precision is particularly crucial in ensuring that the qualitative insights gathered reflect the true sentiments and perspectives of participants. DECOTA’s capability to detect sub-themes alongside broader themes gives it a significant edge, allowing researchers to gain a comprehensive understanding of public opinion that can directly inform policy decisions. This facet of the tool enhances the reliability of the conclusions drawn from survey data, bolstering the integrity of the decision-making process.

The development of DECOTA was initiated to address the urgent need for better analysis of public feedback regarding climate policies, a topic that has gained unprecedented urgency in recent years. However, the applications of this tool extend far beyond environmental issues; it holds the promise of empowering governments and organizations worldwide to tap into public opinion on a myriad of topics such as healthcare, social justice, and economic policy. The versatility of DECOTA is one of its standout features, opening the door for its utilization in a diverse array of contexts where understanding public sentiment is critical.

DECOTA not only identifies key themes within the text but also assesses the demographic factors influencing these opinions. The tool can discern differences in responses among various demographic groups, such as gender and age, providing a nuanced view of public sentiment across different segments of the population. This capability enhances the tool’s usefulness, allowing policymakers to tailor their strategies and communications more effectively to the needs and preferences of different communities.

Transparency is a core principle behind DECOTA’s design. Unlike many traditional analytical tools, DECOTA invites scrutiny at every stage of its processing pipeline. This transparency ensures that researchers can understand, replicate, and, if necessary, amend the analytical processes, fostering an environment of collaboration and trust in the findings produced by the tool. This is particularly important in the realm of qualitative research, where interpretations can often vary significantly and require validation.

In addition to its rapid analysis capabilities, DECOTA includes an intriguing feature where it quotes representative remarks from respondents for each identified sub-theme. This enhances the interpretability of results, allowing researchers and policymakers to not only see numerical data but also understand the human stories and perspectives behind the figures. This qualitative depth is essential for crafting policies that are genuinely reflective of and responsive to public needs.

The project has already attracted attention from various UK governmental organizations, academic institutions, and esteemed global think tanks, highlighting its relevance and applicability. The implications of this tool extend into improving public engagement in policy development, contributing to a more participatory approach to governance. Enhanced data analytics can foster stronger connections between citizens and policymakers, ultimately leading to decisions that are more aligned with the values and priorities of the populace.

The research conducted at the University of Bath exemplifies the intersection of technology and social science, showcasing how advancements in artificial intelligence can yield significant societal benefits. By automating the labor-intensive process of qualitative data analysis, DECOTA not only alleviates pressure on researchers but also paves the way for more responsive governance. As the tool continues to evolve, plans for a more user-friendly web application are in the pipeline, aiming to democratize access to these powerful analytical capabilities for those without coding expertise.

In conclusion, DECOTA represents a promising leap in qualitative research methodologies, enabling rapid, cost-effective, and accurate analysis of public sentiment. Its innovative approach not only enhances the understanding of public opinion but also promotes greater inclusivity in policymaking processes. As we observe the development and application of such tools, it becomes increasingly clear that the integration of AI into social sciences is not just a possibility, but a necessary step towards a more informed, engaged, and responsive society.

Subject of Research: Not applicable
Article Title: The Use of Large Language Models for Qualitative Research: The Deep Computational Text Analyser (DECOTA)
News Publication Date: 7-Apr-2025
Web References: Not available
References: Not available
Image Credits: University of Bath

Keywords

Public opinion, AI tool, qualitative analysis, DECOTA, thematic analysis, natural language processing, public engagement, policymaking, demographic analysis, transparency in research, large language models, social sciences.