by Dr Thineswaran Gunasegaran,
Quest International University
In the rapidly evolving landscape of higher education, generative artificial intelligence (AI) has emerged as both a transformative tool and a subject of intense debate. With its ability to generate code, design solutions, and optimise problem-solving approaches, generative AI is reshaping how students and educators engage with computing and engineering disciplines. However, as with any technological advancement, its benefits come with significant challenges that must be carefully addressed.
Advantages of Generative AI
- Enhanced Learning and Efficiency
AI-powered tools like ChatGPT, GitHub Copilot, Claude, and DeepSeek assist students in writing code, debugging errors, and understanding complex algorithms. These tools provide immediate feedback, enabling a more efficient and interactive learning process.
- Improved Research and Innovation
Generative AI accelerates research by offering data-driven insights, generating hypotheses, and assisting in the simulation of engineering models. For instance, Google’s DeepMind and DeepSeek have demonstrated their ability to solve complex problems in fields like material science and bioengineering.
- Bridging Skill Gaps
AI-powered learning platforms, such as Khan Academy’s AI tutor, Coursera’s AI-enhanced modules, and DeepSeek’s adaptive learning tools, provide personalised study plans tailored to individual needs. This ensures students grasp foundational concepts before advancing to more complex topics.
- Streamlining Software Development
AI-generated code suggestions expedite the development process, allowing students and professionals to focus on higher-level design considerations rather than repetitive coding tasks.
- Enhanced Collaboration
Generative AI facilitates collaboration by improving project documentation, automating code reviews, and assisting in the generation of technical reports.
Disadvantages of Generative AI
- Over-Reliance on AI
A significant risk is that students may become overly dependent on AI-generated solutions, potentially undermining their critical thinking and problem-solving skills.
- Ethical Concerns and Academic Integrity
The ease of generating content raises concerns about plagiarism and originality. Universities are grappling with implementing policies to ensure AI is used as a learning aid rather than a shortcut.
- Accuracy and Reliability Issues
Despite advancements, generative AI models can produce incorrect or misleading information. In fields like computing and engineering, where precision is paramount, reliance on unverified AI outputs can lead to serious errors.
- Bias and Limitations
AI models are trained on existing datasets, which may contain biases. This can result in solutions that favour certain perspectives or reinforce incorrect assumptions, potentially impacting fairness and inclusivity in engineering solutions.
- Privacy and Security Risks
AI tools that require internet connectivity often collect user data, raising concerns about privacy and security, particularly in research projects involving sensitive information.
Comparing State-of-the-Art (SOTA) Generative AI Platforms
Several AI models are making a significant impact in higher education, each with unique strengths and limitations:
| AI Platform | Strengths | Limitations |
| ChatGPT (OpenAI) | Advanced natural language processing, useful for explanations and tutoring | Can generate incorrect answers, and lacks real-time data updates |
| GitHub Copilot | Excellent for code completion and debugging | Sometimes suggests suboptimal or insecure code |
| Google Gemini | Strong in research applications, integrates well with Google’s ecosystem | Less optimized for programming tasks compared to Copilot |
| Claude (Anthropic) | Emphasizes safety and ethical AI usage, provides detailed explanations | More restrictive responses compared to OpenAI models |
| AlphaCode (DeepMind) | Designed specifically for competitive programming | Limited general-purpose application in education. |
| DeepSeek | Focused on adaptive learning and personalized education, strong in coding tasks | Still emerging, limited public data on long-term effectiveness. |
A Balanced Approach
Generative AI undeniably shapes the future of higher education in computing and engineering, offering unprecedented opportunities for learning, research, and innovation. However, educators must strike a balance between leveraging AI’s strengths and ensuring students develop fundamental skills independently. Universities should adopt responsible AI policies, encourage ethical use, and integrate AI literacy into curricula. By doing so, the next generation of engineers and computer scientists can harness the power of AI while maintaining critical thinking, creativity, and ethical awareness—hallmarks of true innovation.
Dr Thineswaran Gunasegaran is Head of Programme for the Diploma in Information Technology at QIU’s Faculty of Computing and Engineering
