7 Trending Topics of Research in Artificial Intelligence
Artificial Intelligence is no longer just a futuristic concept – it is shaping industries, workplaces, and everyday life in real time. From powering chatbots to assisting doctors, from influencing hiring decisions to safeguarding against cyber threats, AI is both a driver of innovation and a subject of growing debate. For researchers, these rapid changes open up exciting opportunities to explore AI’s impact, adoption, and ethical challenges. This blog highlights 7 trending topics of research in artificial intelligence that are making waves in 2025.
1. Affect of Artificial Intelligence on certain jobs
The omnipresence of artificial intelligence (AI) is likely to cause job augmentation where certain job tasks will get automated whereas others will be performed by humans. This topic for research in artificial intelligence explores the potential in the establishment of a hybrid workplace with a high number of human-machine interactions.
Example Survey Questions:
- How useful do you find using AI at your workplace?
- Has AI related tools enhanced your/your team’s productivity at your job related tasks?
Live Example: A researcher from IIM Shillong collected primary data via online survey software in an attempt to investigate the effect of individual characteristics such as personality traits on the predictors of intention to use AI at the workplace.
2. Effectiveness of AI-powered Chatbots
AI chatbots are virtual assistants that help businesses handle customer complaints and resolve issues quickly. When customers face any problems like – wrong order, delayed service, incorrect billing, or product defect – these chatbots are expected to provide immediate and relevant solutions such as refunds, replacements, or troubleshooting steps. In this topic of research in artificial intelligence, researchers can evaluate the effectiveness of AI chatbots in handling and resolving consumer complaints as growing businesses are heavily relying on them.
Example Survey Questions:
- When was the last time you connected with an AI chatbot for complaint resolution?
- How would you describe your complaint resolution satisfaction of AI chatbots vs Human Agents.
Research Tip: Industries like – e-commerce, food delivery, banking and financial Services, travel and hospitality, education, and healthcare and medical services are actively using AI chatbots. Make sure you cover all these industries in your research on artificial intelligence.
3. Exposure, Penetration and Encouragement to use Generative AI at workplace
As the tech industry is embracing the ongoing technology innovation, Generative AI (Gen AI) is going to disrupt the future of work. And some industries have already started embracing it with arms wide open. In this topic of research in artificial intelligence it is important to understand the experience or perception of employees about workplace support and encouragement towards Generative AI, and its ethical usage.
Example Survey Questions:
- Has Gen AI enhanced or replaced my day-to-day work routines?
- Is there a possibility that organisations may use Gen AI unethically?
ThinkSurvey Tip: You can combine the study of adoption of Gen-AI tools and platforms at workplace along with these topics of research on employee satisfaction to explore the outcome that intends to help employers and policy makers with right decision making towards technology innovations vis-a-vis employee retention.
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4. Bias, Fairness, and Ethics in AI Models
As AI systems, applications and tools become integral to hiring, lending, healthcare, and law enforcement, concerns about bias and fairness are growing. This topic on research in artificial intelligence explores how datasets, algorithms, and training processes can be designed to promote equitable outcomes.
Example Survey Questions:
- Do you believe AI systems used in your industry make unbiased decisions?
- Should there be government regulations to ensure fairness in AI algorithms?
Research Tip: Include perspectives from both AI developers and end-users to compare perceptions of bias. Read these 5 must-haves before you start with primary data collection for research, it will give you the right direction to explore issues like usefulness and relevance of current AI models.
5. AI in Healthcare: Diagnosis, Treatment, and Patient Engagement
AI-powered diagnostic tools, predictive analytics, and personalized treatment plans are revolutionizing healthcare delivery. In this topic of research on artificial intelligence, researchers can evaluate accuracy, patient trust, and adoption barriers in AI-assisted healthcare.
Example Survey Questions:
- Have you used AI-enabled healthcare tools (e.g., symptom checkers, diagnostic imaging AI)?
- How comfortable are you with AI making treatment recommendations without a doctor’s review?
Research Tip: Run comparative study among healthcare providers and patients on their perception and acceptance towards use of AI related tools for health diagnosis and treatment.
6. AI and Cybersecurity: Prevention and Threat Detection
With rising cyber threats, financial frauds, and information mishandling; AI-driven systems are now being deployed for threat detection, fraud prevention, and automated incident response. In this tpics researchers can investigate AI’s accuracy, trustworthiness, and cost-effectiveness in cybersecurity operations.
Example Survey Questions:
- Do you trust AI-driven security alerts over traditional security systems?
- Has your organization’s cybersecurity improved after adopting AI tools?
Research Tip: When collecting primary data, segment respondents by industry since AI adoption levels in cybersecurity vary widely between sectors like finance, healthcare, and manufacturing.
7. Environmental Impact of AI and Sustainable AI Practices
While AI offers innovative solutions for climate change and resource optimisation, its own development and use come with significant energy costs. This topic of research on artificial intelligence and Gen-AI investigates public and industry awareness of AI’s carbon footprint, and the willingness to adopt greener AI models.
Example Survey Questions:
- Are you aware of the environmental costs of running AI systems?
- Would you support companies that use energy-efficient AI solutions?
ThinkSurvey Tip: You can combine the study of adoption of eco-friendly AI practices with these environment research topics, using employee perception surveys to measure awareness and policy effectiveness.
How to choose the right research topic on Artificial Intelligence
Choosing the right topic for research in artificial intelligence sets the tone for a productive, publishable career. Use this compact, five-step approach tailored to AI research topics and applied AI projects.
- Survey the landscape: Do an in-depth literature sweep, study recent reviews, top conferences, and preprints to spot trending areas in research topics in artificial intelligence (e.g., fairness, LLMs, edge-AI). Narrow to 2–3 genuinely exciting themes.
- Find concrete gaps: Read Discussion and Future Work sections to extract unanswered questions. Identify specific, timely gaps that matter for theory or real-world systems.
- Test feasibility: Convert gaps into hypotheses and assess skills, data access, compute, budget, and timeline. For gaps needing heavy resources, consider collaborations or scaled-down pilots.
- Ask for feedback: Share top hypotheses with advisors, peers, and potential collaborators. External perspectives reveal prior art, risks, and impact pathways.
- Decide and design: Choose the best hypothesis, draft methods, milestones, and evaluation metrics. Build a plan that balances novelty, reproducibility, and societal relevance.
How to start: A Quick Guide
“I have the idea, but don’t know where to start.”
“I have the questionnaire ready, but stuck at data collection stage.”
We have heard this more often than you would think. If you have ever felt stuck – you are not alone.
Define your target audience, be precise what you want to ask in the questionnaire. For data collection – best would be to go all over the country and cover all the industries of targeting working professionals. With an equal distribution of gender, age groups, income, etc. You can use multiple options like email list for bulk email sending, recruiting on social media or even utilize online survey panel.
As AI continues to evolve, so do the questions it raises – about efficiency, fairness, trust, and sustainability. The topics discussed here are not only relevant today but will likely shape the future of technology, work, and society. Whether you’re an academic researcher, policymaker, or industry professional, investigating these areas can provide valuable insights for responsible AI adoption. Now is the time to design research that doesn’t just follow trends but helps guide them.
