ChatGPT’s expanding horizons demand transparency and human oversight

Artificial intelligence is no longer a supporting tool but a driving force reshaping industries and academic disciplines, says a new comprehensive critical review which examines the sweeping transformation triggered by the evolution of ChatGPT and similar large language models (LLMs).

Published in Computers, the study titled "ChatGPT’s Expanding Horizons and Transformative Impact Across Domains: A Critical Review of Capabilities, Challenges, and Future Directions," the study highlights the promise, limitations, and emerging governance needs of these rapidly advancing systems. Based on applications across natural language processing, education, knowledge discovery, and engineering, the review underscores an urgent call for ethical, technical, and regulatory co-evolution as models grow more autonomous and integrated into high-stakes environments.

Evolving capabilities: From tools to agents

The research traces the evolution of ChatGPT from a conversational interface into a sophisticated ecosystem of agentic AI systems. With expanded context windows, retrieval-augmented generation (RAG), multimodal capabilities, and the ability to integrate external tools, modern LLMs have evolved beyond static chatbots into adaptive agents capable of complex reasoning and decision-making.

In natural language understanding, the review identifies a persistent tension between broad generalization and domain-specific expertise. While newer iterations of ChatGPT can process nuanced contexts and maintain coherence in lengthy interactions, their performance still falls short of the deep reasoning needed for specialized fields such as law, medicine, and advanced research. The authors argue that integrating RAG frameworks and hybrid approaches with structured domain knowledge is essential to closing this gap.

The paper also examines the rapid rise of multimodal models, which combine text, vision, and speech capabilities. This shift has expanded use cases from basic content generation to advanced tasks such as autonomous data analysis, voice-enabled customer support, and interactive educational tools. However, the authors emphasize that greater capability brings greater complexity and with it, heightened risks of errors propagating through multi-step tasks in autonomous systems.

Critical challenges across domains

The review details significant challenges that persist across sectors. Key among them are hallucinations, bias, and the black-box problem. Even as models grow more powerful, their opaque reasoning processes hinder transparency and make it difficult for users to assess the accuracy of outputs. This lack of explainability becomes even more problematic in agentic systems where a single incorrect output can trigger cascading errors in workflows.

In content generation, the authors highlight what they call the “quality–scalability–ethics trilemma.” As organizations scale up AI-driven content production, balancing quality, volume, and ethical responsibility remains a formidable challenge. Without rigorous validation pipelines and human oversight, risks of misinformation and unintentional bias grow.

In the realm of knowledge discovery, the integration of LLMs with knowledge graphs and autonomous research agents presents transformative opportunities for accelerating scientific inquiry. Yet this progress carries the risk of a “crisis of explanation,” where the inability to trace or verify AI-driven hypotheses undermines trust in scientific outcomes.

In education, the technology offers unprecedented opportunities for personalized learning, adaptive curricula, and scalable assessment tools. But these opportunities are paired with risks to academic integrity, critical thinking, and equity. The study introduces the concept of a “pedagogical adaptation imperative,” urging educators to move beyond prohibition and toward strategic integration of AI to foster critical engagement and higher-order reasoning skills.

The review also explores engineering applications, from code generation and debugging in software engineering to integration in building information modeling (BIM), mechanical design, and industrial optimization. Here, AI-driven productivity gains are clear, but so too are risks of over-reliance, skill degradation, and vulnerability to security breaches. The authors propose a “human–LLM cognitive symbiosis,” where human expertise remains central in guiding and supervising increasingly autonomous systems.

Future directions and the need for governance

The study argues for a paradigm shift toward responsible and explainable AI. As LLMs become more autonomous and embedded in critical workflows, governance frameworks must evolve to ensure safety, reliability, and accountability.

Key recommendations include the development of new benchmarks that measure not just accuracy but deep reasoning, contextual understanding, and safe behavior in complex environments. The authors call for built-in ethical controls within AI models to mitigate risks associated with bias, misinformation, and unsupervised automation.

In knowledge-intensive domains, the review stresses the importance of explainability to support scientific validity and reproducibility. Without transparent reasoning pathways, the integration of LLMs into research pipelines risks creating a disconnect between output and verifiable knowledge.

In education, the paper advocates for comprehensive AI literacy programs that prepare students and educators to collaborate effectively with AI tools. Rather than framing AI as a threat, the authors highlight the potential for “co-regulated learning environments,” where human critical thinking complements AI-driven efficiency and personalization.

For engineering and other technical fields, the review recommends a portfolio approach to model adoption, ensuring that human oversight and ethical governance remain embedded in every stage of design, testing, and deployment. As human–agent orchestration becomes the norm, organizational roles must adapt to prioritize strategic supervision, risk management, and cross-disciplinary collaboration.

Technological capability, the authors argue, must evolve in lockstep with ethical frameworks, regulatory oversight, and methodological rigor. Without this balance, the transformative potential of AI risks being undermined by trust deficits, safety failures, and social backlash.

The next phase of AI adoption will be defined by strategic alignment between innovation and governance. Organizations, educators, and policymakers are urged to adopt a proactive approach: investing in ethical safeguards, fostering transparency, and preparing for the complex realities of autonomous AI systems.