Human heuristics, the mental shortcuts that individuals use to simplify decision-making processes, serve as the foundation for training artificial intelligence (AI) models like language generators. It can often build on cognitive shortcuts that prioritize speed over accuracy. In the context of AI-generated language, this can result in the generation of content that lacks nuance, depth, or precision. AI model may resort to make assumptions or generalizations, leading to inaccuracies and misunderstandings. If an AI model strictly adheres to learned heuristics, it may struggle to produce language that goes beyond existing patterns, hindering its capacity to generate fresh and creative ideas. Language is dynamic in nature and undergoes constant changes influenced by cultural shifts, emerging trends, and evolving expressions. Human heuristics, often rooted in historical data, may not capture these dynamic shifts effectively. As a result, AI-generated language may become outdated or fail to reflect contemporary linguistic nuances and cultural sensitivities. In order to mitigate these flaws, researchers and developers must be vigilant in refining AI models which involve implementing rigorous bias detection and correction mechanisms during the model training process, diversifying training datasets to include a wide range of perspectives, and regularly updating models to align with evolving language patterns and societal norms. The recent study delves into this particular research area. It aims to achieve two primary objectives: 1. Human vs. AI Differentiation: The first objective focuses on assessing how well students can distinguish between human-authored content and material generated by AI systems on a similar topic. This investigation aims to shed light on the discernment abilities of students when faced with AI-generated text. 2. Comparing Writing Skill of AI Application: The second objective involves evaluating the writing quality of widely used AI applications by undergraduate students during their class assignments. This comparison likely considers aspects such as coherence, clarity, and overall effectiveness. In conclusion, as the field of AI continues to advance, addressing these issues is crucial for ensuring that AI-generated language remains accurate, unbiased, and reflective of the diversity and dynamism of human communication.