𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗔𝗜 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗼𝗻 𝗔𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗣𝗹𝗮𝗴𝗶𝗮𝗿𝗶𝘀𝗺 The proliferation of generative artificial intelligence (AI) language models is reshaping the educational landscape, influencing students' approaches to creativity and critical thinking. In a comprehensive review by me, I identified the problematic effects of AI language models on academic integrity and student plagiarism. You can read the research paper here: https://www.academia.edu/103839294/The_Impact_of_AI_Language_Models_on_Academic_Integrity_and_Student_Plagiarism_Research 𝗧𝗵𝗲 𝗚𝗼𝗼𝗱 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝗱 1. Learning Tools: AI language models serve as valuable tools for students to explore diverse perspectives, acquire knowledge, and enhance research skills. 2. Concerns and Challenges: Instances of students leveraging AI, like Chat GPT, for plagiarism raise questions about originality and critical thinking. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗨𝘀𝗮𝗴𝗲 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 1. Educational Guidance: Institutions should provide clear guidelines on responsible AI usage. 2. Balancing Act: Striking a balance between innovation and maintaining academic integrity is crucial. #AIinEducation #AcademicIntegrity #ChatGPT #StudentPlagiarism #EdTech #CriticalThinking #ResponsibleAI #HigherEd #InnovationInEducation #ResearchSkills #hassantariqmalik

The progression of Artificial Intelligence (AI) through seven distinct stages. ☢ Symbolic AI: This early stage focused on representing knowledge using symbolic logic. It set the groundwork for AI by developing systems based on explicit rules and logical reasoning. ☢ Machine Learning: Computers began learning from data and making predictions. Machine learning revolutionized AI by enabling computers to learn patterns and make predictions without explicit programming. ☢ Data Mining: With the explosion of data due to the internet, data mining techniques emerged to extract insights from massive datasets, empowering data-driven decision-making. ☢ Knowledge Graphs: Knowledge graphs structured information, enhancing context and understanding within AI systems. They facilitated improved reasoning and inference by representing knowledge as interconnected nodes. ☢ Deep Learning: Neural networks with multiple layers, known as deep neural networks, enabled complex tasks like image recognition and natural language processing. This marked a significant advancement in AI capabilities. ☢ Generative AI: Large language models and generative models, like GPT-3, achieved human-like text generation. This stage introduced possibilities in automated content creation and raised ethical considerations. ☢ Explainable AI: As AI systems became complex and less transparent, the focus shifted to making AI models and systems more interpretable. Explainable AI aimed to address the "black box" issue, ensuring transparency, trust, and fairness. Each stage has contributed to AI's progress, creating a foundation for the development of the next generation of AI systems. https://www.linkedin.com/pulse/7-stages-artificial-intelligence-hassan-tariq-malik #AIStages #EvolutionOfAI #ArtificialIntelligenceJourney #UnderstandingAI #AIAdvancements #SymbolicAI #RuleBasedAI #MachineLearning #DataDrivenAI #DataMining #KnowledgeGraphs #DeepLearning #NeuralNetworks #GenerativeAI #HumanLikeTextGeneration #ExplainableAI #TransparencyInAI #AIProgression #TechInnovation #AIApplications #EthicalAI #ResponsibleAI #BuildingTrustInAI #NextGenAI #AIFoundation #TechInsights