SCCS-UAH

Carlos Mir & Federico Pablo-Martí

 

Introduction

The phrase “Publish or Perish” has long been a mantra in academia, underscoring the pressure on researchers to publish regularly as a condition for obtaining and maintaining their positions and advancing their careers. With the introduction of artificial intelligence (AI) in this context, we are arguably entering an era of “Publish (with AI) or Perish.”

AI can automate routine administrative tasks, freeing teachers from time- and resource-consuming operational burdens. Tools such as learning management systems (LMSs) can automate test grading, student data management, and class scheduling (Huang & Rust, 2018). This increase in efficiency allows teachers to focus on more value-added activities, such as research and teaching improvement.

In addition, AI can offer a more personalized educational experience. Intelligent tutoring systems and AI-based learning assistants can adapt the content and pace of instruction to the individual needs of students (Dabbagh & Kitsantas, 2012). This improves academic outcomes and allows teachers to focus on areas where their intervention is most needed, optimizing the use of their time and resources.

 

Teaching Quality

The creation of advanced teaching materials is another area where AI can have a significant impact. AI-based economic simulators and interactive case analyses can provide students with a deeper and more practical understanding of economic concepts (Siau, 2017). Professors who integrate these technologies into their courses can offer a more innovative and engaging education, thus enhancing their reputation and academic competitiveness.

AI systems enable continuous and more accurate assessment of student performance, providing instant and detailed feedback. This improves student learning and allows teachers to adjust their teaching more effectively, contributing to a constant improvement in educational quality. Current LLM models allow for the customization of content according to the special needs of students, as demonstrated by the recent Gemini 1.5. Ultra, and this on demand, without the need for direct intervention by the teacher.

 

Research and Publication

The use of artificial intelligence (AI) is significantly increasing the capacity for quality scientific production, enabling advances that would hardly have been possible without this technology. For example, the COSMIC DANCE project used machine learning techniques to understand the formation and evolution of stars, while the DrugComb project used mathematical and computational tools to identify drug combinations for personalized cancer treatments. AI has also optimized the design of new materials and improved the classification and analysis of seismic signals, as seen in the TopMechMat and F-IMAGE projects, respectively (CORDIS EU).

These initiatives highlight how AI not only automates tasks and analyzes large volumes of data, but also facilitates the identification of complex patterns and the generation of new scientific knowledge, pushing the boundaries of research and accelerating discovery ().

The use of artificial intelligence (AI) in economic research enables breakthroughs that are difficult to achieve due to the limitations of traditional methods. AI facilitates the analysis of large volumes of data, allowing researchers to identify complex patterns and develop more accurate predictive models. For example, a recent study used machine learning to analyze historical price data and predict future trends in the financial market, achieving significantly greater accuracy than conventional models (McKinsey, 2020) (McKinsey & Company).

Another prominent application of AI in economics is the analysis of fiscal and monetary policies. Researchers have used AI algorithms to assess the impact of different economic policies in real time, allowing for more accurate and efficient adjustments. A notable example is the use of neural networks to model the impact of interest rate changes on economic growth and inflation, providing detailed insights that better guide policy decision making (Nakamura and Steinsson, 2018).

In addition, AI has been used to improve the quality of studies on economic inequality. Natural language processing (NLP) algorithms have been employed to analyze large corpora of text, such as economic publications and government documents, identifying trends and correlations that were previously not visible. These studies have revealed new insights into the causes and consequences of economic inequality, facilitating the creation of more effective policies (Gentzkow, Kelly, and Taddy, 2019).

The ability of AI to analyze unstructured data, such as social networks and Internet search data, has also revolutionized the field of behavioral economics. Researchers have used these techniques to study consumer behavior and predict market reactions to economic events, improving the ability to anticipate changes in demand and adapt business strategies accordingly (Einav and Levin, 2014).

In short, AI is revolutionizing research in economics by providing advanced tools for data analysis and predictive modeling. This not only increases the amount of high-quality research, but also pushes the boundaries of economic knowledge, allowing economists to tackle complex problems with unprecedented accuracy and efficiency. The adoption of AI technologies is becoming a necessity for those seeking to remain competitive and relevant in the field of economics.

AI also fosters interdisciplinary collaboration. Many economic problems can benefit from the advanced data analysis and modeling offered by AI. Faculty who develop competencies in AI will be better positioned to participate in collaborative research projects, increasing their visibility and competitiveness (Autor et al., 2020).

In the “publish or perish” era, academics faced constant pressure to publish high-quality research on a regular basis. However, with the advent of AI, this pressure has taken on a new dimension. Now, researchers must not only publish, but must do so using advanced AI tools if they want to remain competitive and relevant in their field.

 

Spanish Research Accreditation and Evaluation System (ANECA)

The system of accreditation and evaluation of research in Spain, managed by ANECA (National Agency for Quality Assessment and Accreditation), establishes strict criteria for the evaluation of university faculty. ANECA assesses aspects such as the quality of research, teaching and academic management (ANECA, 2024). In this context, the integration of AI can significantly influence the fulfillment of these criteria.

In order to obtain ANECA accreditations, it is essential that professors publish (high quality) research in recognized journals. The ability of AI to facilitate the analysis of complex data and improve the accuracy of research can result in higher impact publications, crucial for ANECA evaluation (ANECA, 2024).

ANECA also values innovation in teaching. Professors who use AI tools to improve teaching and personalize student learning can demonstrate a dedication to continuous improvement and adoption of new technologies, favoring their positive evaluation (ANECA, 2024).

Publish or Perish

The “publish or perish” phenomenon describes the constant pressure faced by academics to regularly publish their research and advance their careers. AI can be a valuable tool by enabling faster and more accurate analyses and facilitating interdisciplinary collaboration (Brynjolfsson & McAfee, 2014).

AI techniques can help researchers identify new areas of study, analyze data more efficiently and write articles faster. This not only increases the quantity of publications, but also their quality, essential in a competitive environment where publication in high-impact journals is crucial for professional promotion and consolidation (Varian, 2014).

Skills and Adaptability

Despite the significant advances that artificial intelligence (AI) is bringing to various sectors, academia, and in particular economics professors, a notable resistance to its adoption. This resistance, while understandable, can have profound and lasting consequences on the competitiveness and relevance of academics.

Let’s imagine Ruth, an economics professor at a Spanish university. Ruth has been teaching and conducting research for more than a decade, using traditional methods that have been effective so far. However, the emergence of AI is rapidly changing the academic landscape. Ruth hears about colleagues who are intensively using AI tools to automate administrative tasks, personalize student learning, and perform complex data analysis for their research. Although she recognizes the potential benefits, Ruth is intimidated by the technology. She has no background in programming or data analysis and wonders how she could integrate AI into her already busy schedule. The speed in the development of “commercial” AI models in the form of LLMs and ML means complexity in selecting the most appropriate models and integrating them into her daily workflow.

This situation is common among many teachers. Lack of specific training in AI is a significant barrier. Learning new technologies can seem like an overwhelming task, especially when already faced with substantial workloads and high academic expectations. This lack of technical skills can make AI seem inaccessible and out of reach for many academics, who may fear that its use requires a level of competence they simply do not possess (Brynjolfsson & McAfee, 2014).

In addition, fear of change is a powerful factor. The introduction of AI represents not just a new tool, but a potentially disruptive change in the way teaching is taught, research is conducted, and day-to-day tasks are managed. For some, this change is viewed with skepticism and suspicion. Could AI replace aspects of their work? Will it change the nature of teaching and research in fundamental ways? These questions generate a natural resistance to change, based on uncertainty and anxiety about the future (Henderson et al., 2017).

Another critical aspect is uncertainty about how to integrate AI into day-to-day work. Even for those who see the value in AI, the lack of clear guidance on implementation can lead to inaction. Integrating AI requires significant adaptation: from modifying teaching methods to redesigning research procedures. Without a clear vision and implementation plan, many may feel lost and overwhelmed, opting to continue with traditional methods (Huang & Rust, 2018). However, this involves the development of new research methods that represent a barrier to entry. There are many teachers who face computational difficulties and code development in R or Python, something that is no longer a limitation with the new tools.

Lack of technical skills and fear of change are significant barriers. The introduction of AI represents a disruptive change in the way we teach, research, and manage daily tasks. Without clear guidance on implementation, many academics may choose to continue with traditional methods (Huang & Rust, 2018).

However, resistance to adopting AI can have serious consequences. In the competitive world of academia, professors who do not adopt AI risk falling behind. AI can significantly increase efficiency, freeing up time for deeper research and creating more enriching learning experiences (Varian, 2014).

In the Spanish context, ANECA plays a crucial role in the careers of academics. ANECA evaluation values innovation in teaching and research quality. Professors using AI can demonstrate a greater capacity to produce high quality research, favoring their positive evaluation (ANECA, 2021). But ANECA also has to be aware of the new panorama it faces in curriculum evaluation, the greater diversity of areas where the researcher can participate and the weight that this type of tools has had and will have in the research processes.

 

Ethical Challenges and Considerations

The use of AI raises ethical and privacy challenges, especially in the management of student data and automated decision making. It is crucial that faculty are aware of these issues and work to ensure that the use of AI is transparent and respectful of students’ privacy and rights (Tegmark, 2017). Universities should establish clear and ethical policies for the use of AI.

To overcome these challenges, it is essential that academic institutions provide the necessary support. Universities should offer training and professional development programs that help teachers acquire the necessary skills to use AI. These programs can include courses in programming, data analysis, and practical applications of AI in teaching and research (Henderson et al., 2017).

In addition, universities should provide access to AI tools and technology resources, making adoption more accessible. This includes data analytics software, automated learning platforms, and access to relevant databases. By providing the necessary resources and fostering a culture of innovation, institutions can reduce resistance to change and ease the transition to AI use (Siau & Yang, 2017). Subscribing to this new world cannot fall on the solvency of the teacher, but be part of their technological infrastructure that they use on a day-to-day basis away from physical infrastructures that take on a secondary role. It requires teachers’ lounges, which can facilitate the development of complex research by moving the teacher away from individual work in his office.

The availability of resources to implement AI can vary significantly across institutions, exacerbating existing inequalities in access to advanced technologies. Faculty at institutions with fewer resources may find it more difficult to remain competitive (Vincent-Lancrin et al., 2019). Ensuring that all faculty have access to the tools and resources needed to integrate AI into their work is critical.

Conclusion

The new Publish (with AI) or Perish framework has the potential to significantly transform the academic landscape, offering both opportunities and challenges. The key to making the most of this transformation lies in finding the right balance between adopting new technologies and preserving the fundamental tenets of academic research: integrity, rigor, and originality. It will be essential to develop ethical policies and practices to guide the use of AI in research to ensure that these tools are used in a way that benefits the academic community as a whole, without compromising the quality and authenticity of scientific output. The ANECA evaluation system adds pressure and opportunity in this context. The ability to produce high quality research and to innovate in teaching is crucial for obtaining accreditations and advancing in academic careers.

It is essential that the academic community address these challenges proactively, collaborating to establish standards and guidelines to ensure ethical and equitable use of AI technologies. In addition, education and training programs should be encouraged to enable researchers to use these tools effectively and ethically.

 

References

• ANECA. (2021). Evaluation criteria. Retrieved from ANECA.
• Autor, D., Mindell, D., & Reynolds, E. (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. MIT Work of the Future.
• Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.
• Dabbagh, N., & Kitsantas, A. (2012). Personal Learning Environments, social media, and self-regulated learning: A natural formula for connecting formal and informal learning. The Internet and Higher Education, 15(1), 3-8.

• Einav, L., & Levin, J. (2014). “Economics in the age of big data.”

 

• Gentzkow, M., Kelly, B., & Taddy, M. (2019). “Text as Data.”

• Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567-1579.
• Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155-172.

• McKinsey & Company (2020). “How AI is revolutionizing financial services.”
• Nakamura, E., & Steinsson, J. (2018). “Identification in Macroeconomics.”

• Siau, K (2017). Impact of Artificial Intelligence, Robotics, and Automation on Higher Education. Proceedings of the International Conference on Information Systems Education and Research, 1-5.
• Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.
• Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3-28.
• Vincent-Lancrin, S., Urgel, J., Kar, S., & Jacotin, G. (2019). Measuring Innovation in Education 2019: What Has Changed in the Classroom?. OECD Publishing.