AI Boost Academic Performance using Case study approach of Economics and Finance

By: Ankita Manohar Walawalkar, Department of Business Administration, Asia University;


This article analyses the possibility of Artificial Intelligence (AI), specifically Natural Language Processing (NLP), to increase academic attainment by exploring one case study in economics and finance. Also, consider whether an AI-based manuscript undermines the researcher’s originality. It addresses the potential of ChatGPT in academic research, predominantly in economics and finance, but also highlights limitations such as generalizability, data quality, domain expertise, context understanding, and ethical considerations. According to finding it can be seen that, when using ChatGPT, it is critical to keep these boundaries in mind and use it in tandem with human analysis and interpretation.


AI mainly NLP, has the potential to transform academic research, with ChatGPT, a ChatGPT (Generative Pre-training Transformer) variant established by OpenAI, being a distinguished example [1]. Researchers can refine creative work with AI support, but selecting, evaluating, and adjusting AI is crucial. Even if AI can perform tasks, researchers must evaluate its performance to protect originality. Study explores the usage of ChatGPT, in theoretical investigation, addressing potential biases as well as ethical considerations, and discussing future developments and implications [2].

Exploring the Potential Uses of ChatGPT in Economics and Finance.

ChatGPT assists in the generation of reports, summaries, and estimates of economic and financial data, enabling researchers and analysts to better comprehend and communicate findings. It analyses vast data sets to detect trends, increase the accuracy of economic and financial models, present real-time reporting, and improve consumer behaviour, risk assessments, and financial forecasting [3]. It can create simulations and scenarios to test and evaluate various economic and financial ideas and tactics, includes creating estimates and predictions based on historical information. ChatGPT making it easier to understand and communicate its human-like text in a conversational style also makes it a model tool for intermingling with customers and stakeholders which will help further for valuable for decision-making purposes[1].

The Utilization of Advanced Bots in Research: Benefits and Implications

The usage of ChatGPT and other advanced chatbots in research has several implications. For instance, it can increase research efficiency by automating specific tasks and processes [3]. It enhances research accuracy by recognizing and correcting errors in data or analysis. It can allow greater flexibility in regards to the kinds of research questions that might be asked. It could potentially improve research procedures. It allows researchers to gain more objective insights by reducing the influence of personal biases or subjective assessment. Also, can offer researchers greater consistency[1].

Considering ChatGPT’s restrictions

The excellence and relevancy of the information utilise to train ChatGPT and other progressive chatbots has an enormous effect on their ability to perform. Biased, incomplete, or otherwise faulty training data may hurt the chatbot’s performance. Limited understanding of economics and finance could impede accurate data analysis and interpretation. Furthermore, this kind of research involves a number of moral fears, such as the prospect of bots substituting humanoid labor or perpetuating biases implicit in the data upon which they are trained[2]. It might be used for immoral or malicious targets, such as sending spam or impersonating other users. Moreover, Performance and capacities depend on the state of fundamental technology. It can be seen that the language model may not have sufficient framework about the topic at hand, and therefore may create answers that are discrete or generic [1].

Ethical considerations and potential biases while using ChatGPT or equivalent methods for academic research.

Using ChatGPT or identical technologies for academic research brings up many kinds of ethical challenges and potential biases that needs to be carefully assessed. Some of these include:

a. Algorithmic bias: ChatGPT, among various other technologies, may be trained on big datasets including potential biases or mistakes, propagating stereotypes or generating biased results. These biases must be considered and addressed the model to be unbiased and unbiased [3] .

b. Transparency and accountability: ChatGPT depend on complicated algorithms may be problematic to hold answerable for biases or mistakes. Transparency and examination of processes for making decisions are essential for fairness [4] .

c. Human oversight and interference: It necessitates human attention and involvement to function properly, emphasizing the importance of human intervention in rectifying errors or biases[2].

d. Data privacy and security/Responsible use of data: Academic research employing ChatGPT creates privacy and security problems, requiring suitable safeguards that shield users’ data from unauthorized access or utilization [4].

e. Impact on employment: ChatGPT usage in academic research has an opportunity to influence employment by automating tasks or altering skill sets, necessitating cautious and ethical deployment to ensure job market stability[5].

f. Implications for research quality: It may enhance academic research, but their drawbacks must be acknowledged in order to prevent replacing human judgment and impacting research culture and practices [6].

g. Accuracy and reliability: ChatGPT and other technologies should be evaluated for precision and dependability, taking into account any possible biases or restrictions that may affect their consequences [7] .

Figure 1: Ethical Considerations and Potential Biases in Academic Research Using ChatGPT


AI , ChatGPT and NLP can assist academics in more professionally and efficiently processing and analysing enormous quantities of information, creating practical situations for challenging and assessing models, and expressing their discoveries in a vibrant and sane format. These competences have the latent to knowingly quicken investigate in a range of domains, resulting in novel discoveries and visions that may transform our knowledge of the world. With the appropriate tools and methodologies, AI and NLP technologies can considerably recover the efficacy and productivity of speculative investigation, top to novel findings and visions that can shape the future.


  1. M. M. Alshater, “Exploring the Role of Artificial Intelligence in Enhancing Academic Performance: A Case Study of ChatGPT.” Rochester, NY, Dec. 26, 2022. doi: 10.2139/ssrn.4312358.
  2. E. Nakazawa, M. Udagawa, and A. Akabayashi, “Does the Use of AI to Create Academic Research Papers Undermine Researcher Originality?,” AI, vol. 3, no. 3, Art. no. 3, Sep. 2022, doi: 10.3390/ai3030040.
  3. P. P. Ray, “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet Things Cyber-Phys. Syst., vol. 3, pp. 121–154, Jan. 2023, doi: 10.1016/j.iotcps.2023.04.003.
  4. R. Peres, M. Schreier, D. Schweidel, and A. Sorescu, “On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice,” Int. J. Res. Mark., vol. 40, no. 2, pp. 269–275, Jun. 2023, doi: 10.1016/j.ijresmar.2023.03.001.
  5. M. Rahman, H. J. Terano, M. Rahman, A. Salamzadeh, and Md. S. Rahaman, “ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples,” vol. 3, pp. 1–12, Mar. 2023, doi: 10.52631/jemds.v3i1.175.
  6. “Unveiling the relevance of academic research: A practice-based view – ScienceDirect.” Accessed: Feb. 26, 2024. [Online]. Available:
  7. D. Johnson et al., “Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model,” Res. Sq., p., Feb. 2023, doi: 10.21203/

Cite As

Walawalkar A M (2024) AI Boost Academic Performance using Case study approach of Economics and Finance, Insights2Techinfo, pp.1

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