Exploring the Impact of Large Language Models on Efficiency and Innovation across Education and Industries: Applications, Challenges, and Ethical Considerations

This paper explores the emergence of Large Language Models (LLMs) in combination with easy-to-use interfaces such as ChatGPT and Copilot revolutionizing productivity in computing and processing. We conducted a user study in the education and industries to assess its potential impact and the opportunities of innovation. Specifically, the key challenge for LLM is to determine what constitutes a good text and natural language processing (NLP) techniques within massive computational infrastructures to communicate efficiently and affect institutions’ innovation outputs.

Keywords: Large Language Model; ChatGPT; Knowledge Absorptive Capacity; Natural Language Processing

Computing Processing of NLP and LLMs

In recent years, revolutionary progress in natural language processing (NLP) has led to the rise of large language models (LLMs), exhibiting exceptional abilities in various domains such as computing, processing, communication, networking, and broadcasting. Prompt engineering, a modern field, centers on crafting and refining prompts to maximize the efficient utilization of large language models (LLMs) across a wide range of applications and research areas [1]. AI-powered language tools, built on top of Large Language Models (LLMs), are enhancing productivity in various domains, including communication, networking, and broadcasting. OpenAI’s GPT, a leading LLM, excels in natural language processing tasks.

OpenAI’s GPT and GitHub Copilot

The emergence of Large Language Models (LLMs) alongside user-friendly interfaces, such as OpenAI’s GPT [2] series and GitHub Copilot [3], marks a significant milestone in natural language processing and human-computer interaction. These advanced models demonstrate exceptional proficiency in understanding and generating human-like text, while user-friendly interfaces facilitate smooth interactions. Consequently, LLMs are increasingly penetrating fields such as business, information systems engineering, and software development, with the goal of streamlining communication, enhancing productivity, and fostering innovation. ChatGPT rose to prominence as a trending topic on the internet towards the close of 2022, solidifying its status as a cultural sensation [4]. It garnered widespread attention and acclaim, with numerous sources citing its impact and influence.

However, despite the rapid evolution and widespread adoption of LLMs and user-friendly interfaces, several critical research gaps persist. These encompass the ethical and societal implications of AI-driven language technologies, the dynamics of human-LLM interaction, the imperative for customization and personalization, and the enduring effects on workforce dynamics and employment opportunities [5]. Therefore, our goal is to enhance understanding in the fields of NLP, human-computer interaction, and AI ethics. Ultimately, we aim to promote informed decision-making and ethical innovation in AI technology for enhanced cross-cultural communication and understanding [6].

The Role of Large Language Model

Knowledge absorptive capacity is crucial for organizations to integrate and utilize external knowledge, driving innovation. This capacity enables the acquisition, collection, and processing of diverse organizational knowledge [7,8]. The advent of advanced Natural Language Processing (NLP) technologies, particularly Large Language Models (LLMs) like OpenAI’s GPT series and others, has significantly advanced this field. These models, trained on extensive datasets, excel in tasks such as text generation, sentiment analysis, and question answering, offering powerful tools for handling, interpreting language data [9,10] and possibly improving knowledge absorptive capacity.

LLMs like ChatGPT and GitHub’s Copilot represent the cutting edge of NLP technology, with specific applications in different fields. ChatGPT, developed by OpenAI, focuses on understanding and generating human-like text, making it valuable for tasks requiring natural language comprehension. In contrast, Copilot serves as an AI assistant for software developers, generating code snippets based on contextual cues [11,12]. These tools demonstrate the transformative potential of LLMs in both enhancing productivity and supporting complex decision-making processes, though they also raise important considerations around ethical use, accuracy, and data privacy.

Semi-structured in-depth interviews were utilized in eliciting detailed insights relevant to GPT models’ real-world applications and implications. The semi-structured format of the interview enables 5 participants to express their experiences from education, construction, telecommunication, processing and manufacturing, and commercial banking. Application scenarios include teaching improvement, design concept generation, customer communication, project management, smart customer service, and market analysis.

By using purposive sampling, five participants were recruited in Taiwan, each with 10 to 30 years of work experience across education and industries employing between 20 to 900 staff members. Their educational backgrounds included two PhD, two Bachelor and one Master (Table 1). Interviews were transcribed, and then analyzed using AI-based automatic content analysis tools, including ChatGPT [13].

Current research primarily concentrates on the capabilities and performance of Large Language Models (LLMs). However, there’s a lack of understanding regarding how individuals interact with these systems, especially through user-friendly interfaces. Exploring the impact of user experience and interface design on human-LLM interaction can provide valuable insights for applying these technologies (Table 2).

Table 1: Profile of Interviewees.

Table 2: Opportunities and Challenges of Using LLMS across Education and Industries.

Architects can use ChatGPT/Copilot

To enhance efficiency, design innovation and client communication through natural language processing. However, ensuring algorithmic accuracy and overreliance on these tools pose challenges. Architects must also address ethical considerations, ensuring that AI models complement human creativity and judgment while navigating computational decision-making with prudence.

University professors can utilize ChatGPT/Copilot

In academia to enhance efficiency in teaching and research by automating tasks like generating lecture materials and conducting data analysis. However, professors must navigate challenges such as ethical concerns, dependency risks, bias, and fairness. While these tools maintain academic integrity regarding plagiarism and data privacy remains paramount, requiring careful consideration and oversight.

Senior telecom executives can use ChatGPT/Copilot

Streamlining potential in decision-making, customer service and employee management. However, challenges arise in telecom research due to concerns like data privacy, model bias, and regulatory compliance. Sharing sensitive information with ChatGPT/Copilot may pose data privacy risks, highlighting the importance of stringent data handling practices to ensure compliance and mitigate potential privacy breaches.

Senior managers in the processing manufacturing

Industry can view ChatGPT/Copilot as an intelligent production management and decision support tool to optimize production processes, enhance efficiency, lower costs, elevate product quality, and fortify supply chain management. The challenges for senior managers in the processing manufacturing industry include technical integration issues, data security concerns, technological limitations, employee acceptance hurdles, and legal compliance requirements when implementing ChatGPT.

Commercial bank employees can utilize ChatGPT/Copilot

As intelligent assistants, enhancing customer service, risk management, and business performance. Challenges include grasping their limitations, maintaining critical thinking, and ensuring decisions align with real-world contexts. Overcoming these hurdles is essential for leveraging ChatGPT effectively, ensuring that its outputs complement human expertise and contribute to informed decision-making in banking operations.

In summary, ChatGPT/Copilot can enhance employees’ knowledge absorptive capacity and communication by efficiently assimilating and applying information in various tasks. However, challenges such as understanding the limitations of LLMs, fostering critical thinking, and ensuring alignment with real-world contexts emerge. Balancing the opportunities presented by LLMs with these challenges is crucial for maximizing knowledge absorptive capacity and driving innovation within organizations.

In conclusion, this paper evaluates the effectiveness and limitations of ChatGPT/Copilot, offering efficiency benefits for architects, professors, senior telecom executives, and manufacturing managers. However, it’s crucial to acknowledge its limitations and navigate ethical concerns. Challenges include accuracy, overreliance, data privacy, and regulatory compliance, highlighting the importance of careful handling. Effective communication skills and employee market knowledge absorptive capacity are essential in addressing these challenges and maximizing the entrepreneurial opportunity presented by AI technologies in diverse industries.

The authors acknowledge the usage of ChatGPT in terms of refining and editing the manuscript.

No conflict of interest.

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