Part 1:

I think most of university students are getting more lazy as the AI tools evolves. They put too musch effort in humanizing AI generated content while almost disregarding learning outcomes. Last year, I was exploring ChatGPT and asked it explain some chemistry concepts giving examples and the results were extremely awful. Also, i found out that some of my friends use it to solve their assignments and summarize course materials in order not to “waste time” studying a whole chapter. To be honest, Iam not familiar with all Ai tools but I believe it is ridiculous to expect ChatGPT or similar AI tools to provide you with the right answers for mathematics, physics, or chemistry problems. 

 

Part 2:

ChatGPT: https://chatgpt.com/share/6715d83b-63a0-8008-8a7a-0462091a8109 

 AI tools are transforming the field of chemistry by enhancing research, accelerating discovery, and optimizing processes. Below are the key areas where AI is making an impact:

1. Drug Discovery and Pharmaceutical Chemistry

  • Molecular design and prediction: AI models like generative algorithms help create novel molecules with specific properties (e.g., antiviral compounds).

  • Virtual screening: Machine learning models rapidly screen large chemical libraries to identify candidates with therapeutic potential.

  • Optimization of drug synthesis: AI helps find the most efficient synthetic pathways, reducing time and cost for pharmaceutical production.

Example: AlphaFold, an AI by DeepMind, predicts protein structures with unprecedented accuracy, aiding drug-target interactions.

2. Material Science and Catalysis

  • New materials discovery: AI helps identify novel polymers, nanomaterials, and alloys with desired properties (e.g., superconductivity, flexibility).

  • Catalyst design: Machine learning optimizes catalytic reactions by predicting which materials would increase efficiency.

Example: AI accelerates the discovery of materials for batteries and solar cells by simulating properties like conductivity and stability.

3. Chemical Synthesis Optimization

  • Predictive reaction models: AI predicts yields, reaction times, and side products, minimizing trial-and-error experimentation.
  • Retrosynthetic analysis: Algorithms identify the best way to break down complex molecules into simpler starting materials, guiding chemists in synthesis.

4. Environmental and Green Chemistry

  • Pollution monitoring and mitigation: AI-powered sensors analyze air and water quality to detect pollutants more accurately.
  • Optimizing sustainable processes: AI suggests greener synthesis routes and reduces the environmental impact by optimizing energy use in chemical plants.

5. Computational Chemistry and Quantum Chemistry

  • Molecular dynamics simulations: AI speeds up simulations of chemical interactions and molecular behavior.
  • Quantum chemistry models: Machine learning enhances calculations that predict the electronic structure of molecules, enabling precise property predictions.

6. Chemical Data Analysis and Automation

  • Data mining: AI analyzes vast datasets from chemical experiments, papers, and patents to identify trends and insights.
  • Robotic chemistry labs: AI-guided robots perform repetitive experiments, reducing human error and increasing efficiency.

Example: Robo-chemists, such as IBM’s RXN, automate synthetic chemistry experiments with AI-generated protocols.

7. AI in Education and Research

  • Virtual labs and simulations: AI-powered platforms provide students with realistic virtual experiments.
  • Assistance in research: AI tools like natural language processing (NLP) help chemists stay updated by summarizing research papers and patents.

AI is driving the future of chemistry by reducing experimental bottlenecks and enabling faster, cost-efficient solutions for scientific challenges. As these tools evolve, they are expected to open new frontiers in chemistry that were previously unimaginable.

 

Parts 3&4:

Link for the interviews: https://drive.google.com/drive/folders/1friJj0zrPB8Ag8PJD4cS6cLon4wtMHKi?usp=sharing

  1.  Dr. Hassan discussed AI’s role in research, noting that some international publishers reject AI-assisted papers. He highlighted the case of Laila, who used AI to design a drug, optimizing efficiency and minimizing side effects, with the AI guiding her through every step including chemical structure, commercial naming, and experimental plans. He expressed amazement at AI’s persistence and adaptability in answering questions. He emphasized that AI offers a strong starting point by proposing reasonable ideas, but success depends on asking the right questions and using effective synonyms. While his team hasn’t fully explored AI yet, he advocates for learning and educating others on its potential rather than restricting its use. AI should support innovation, optimization, and guidance, though its outputs must always be verified

  2.  Hania, a TA, found it frustrating to grade lab reports copied from AI, as they used outdated language uncharacteristic of her students. She stressed that lab reports should reflect students’ understanding. While she supports AI for brainstorming and proofreading, she opposes its use for plagiarism. As a researcher, she values AI for gathering literature, brainstorming, and explaining concepts.

 

Part 5:

To be honest I never imagined AI could really help that way especially for delivering innovation that are based on very detailed scientific processes. The answers I got from ChatGPT was very promising and I thought It is just biased until I interviewed Dr. Hassan. He emphasized the significance of AI in scientific research, expecting a larger scale of applications in the future using AI. For AI in plagiarism at AUC, Dr hassan mentioned nothing about that, so may be he did not encounter this before. However, as a student I know it happens that some students use AI to solve their homework, and also I got shocked to hear from Hania that they use it also for doing their lab reports. I it crazy, how students misuse this powerful tool and totally neglect enhancing their skills and gaining knowledge. I believe that AI tools should not be accessible to everyone to minimize the misuses especially in academics. On the other hand Dr. Hassan does not think that this would be a solution because at the end students will find a way to access AI and he advocates for educating people rather that preventing them. Overall, I it a bout personal ethics and what we need to achieve, personally, I used to hate AI but I am now surprised by its capabilities in Chemistry and I think I need to add “learn AI” to my Winter holiday plan 🙂