Two-day Faculty Development Programme

Harnessing AI Tools for Deeper Research

Sandipani Leadership Development Centre, Lavale Campus, Pune · 08–09 December 2025

Context & Purpose

Reimagining the Researcher’s Workflow in an AI World

Generative AI is no longer a distant trend—it is quietly reshaping how scholars discover literature, collect data, analyse results, and format manuscripts. Around the world, faculty development initiatives are being built to help academics explore AI responsibly and creatively in their research practice.

Against this backdrop, Symbiosis Law School, Pune hosted a two-day Faculty Development Programme (FDP) on “Harnessing AI Tools for Deeper Research”, bringing together 33 faculty members from multiple institutes of Symbiosis International (Deemed University). Over 12 hours of structured, hands-on sessions, the cohort explored how AI can enhance the quality, speed, and depth of research across the full lifecycle—from literature review to final formatting.

Curriculum Architecture

12-Hour Capability Blueprint

A structured, 8-module learning journey transitioning professionals from foundational AI awareness to advanced, ethically governed research execution.

01

AI Landscape & Ecosystem Navigation

Establish a foundational understanding of how modern artificial intelligence is revolutionizing data workflows. Navigate the evolving pool of available AI utilities and establish readiness for adoption.

Ecosystem Overview Workflow Integration
02

Accelerated Knowledge Discovery

Deploy intelligent agents to execute concept mapping, literature discovery, and thematic trend analysis effortlessly across vast data repositories.

OpenAlex Consensus AI SciSpace ResearchRabbit
03

Advanced Prompt Engineering

Master the science of prompting. Engage in hands-on optimization techniques to achieve high-fidelity, context-aware outputs from foundational Large Language Models.

ChatGPT Perplexity Gemini Co-pilot
04

Automated Data Acquisition

Streamline and automate the survey deployment and raw data collection pipelines to drastically reduce manual administrative overhead.

SurveyMonkey Google Forms Forms.app Question Pro
05

Unstructured Audio Processing

Leverage deep neural networks to extract, transcribe, and analyze qualitative data from spoken words, meetings, and unstructured audio environments.

Otter.ai MS Teams Galaxy AI
06

AI-Augmented Data Analysis

Utilize intelligent assistants to conduct robust quantitative and qualitative data analysis, accelerating the transition from raw data to actionable insight.

Orange Data Mining JASP Julius Quadratics
07

Intelligent Synthesis & Presentation

Automate the final stages of output generation. Utilize advanced tools for formatting, citation management, and the professional presentation of finalized data.

Microsoft Co-pilot Zotero Grammarly
08

Ethical Governance & Compliance

Ensure sustainable and compliant AI adoption. Navigate the complexities of intellectual property, detection software, and mindful implementation practices.

GPTZero Compliance Frameworks Sustainable Adoption
FDP Evaluation Index

Validation Ledger from Senior Academia

Verified verification assets and raw participant feedback, captured live during the execution of our "Harnessing AI for Deeper Research" L&D initiatives.

FAQ

Frequently Asked Questions

How does this program differentiate between generative AI and academic research intelligence?

Standard generative models (like basic ChatGPT) are prone to hallucinations and lack rigorous source tracing. Our architecture strictly separates ideation models from execution models. We train researchers to utilize scientifically grounded tools (like Consensus AI and OpenAlex) that strictly mine peer-reviewed data, ensuring institutional integrity.

Will using these AI frameworks trigger plagiarism or AI-detection software (e.g., Turnitin)?

We teach AI as an augmentation tool, not a ghostwriter. The curriculum focuses on ethical prompting where the AI assists in structural outlining, data synthesis, and grammatical refinement. We dedicate specific modules to governance, ensuring original intellectual property remains intact and detectable thresholds are neutralized.

Explore the Plagiarism-Safe AI Writing Framework
How do we ensure the accuracy of AI-generated citations and references?

Citation hallucination is the highest risk in AI-assisted research. We bypass this by integrating dedicated citation managers (like Zotero) with AI discovery platforms. We train faculty on a strict verification loop that ensures every generated DOI and APA/IEEE reference matches the verified source text exactly.

Review our Citation Accuracy Checklist
What is the learning curve for senior faculty or executives with low digital fluency?

The program is engineered with Andragogy (adult learning principles) at its core. We do not assume advanced technical knowledge. The capability journey starts with simple, high-impact tasks and gradually introduces complex data analysis, ensuring participants build confidence through hands-on, contextual practice rather than intimidating theoretical lectures.

Read the AI Guide for Beginner Researchers
Does the curriculum cover automated literature discovery and gap identification?

Yes. Manual literature reviews are notoriously time-intensive. We teach participants how to deploy AI agents (like ResearchRabbit and SciSpace) to visually map academic relationships, instantly summarize dense PDFs, and identify hidden research gaps across thousands of global publications in a fraction of the time.

See the AI Literature Review Workflow
How can AI accelerate the quantitative and qualitative data analysis phases?

We transition researchers from manual coding to intelligent automation. For quantitative data, we demonstrate AI integrations that instantly generate statistical models and visualizations. For qualitative data, we utilize neural networks to transcribe, code, and extract thematic insights from unstructured text and interview audio.

Can prompt engineering actually reduce administrative and research bottlenecks?

Absolutely. The difference between an amateur AI user and a professional is the quality of the prompt context. By learning role-based, context-rich prompting, researchers can automate repetitive tasks like drafting grant proposals, formatting compliance documents, and generating peer-review rubrics.

Access 25 Time-Saving Prompt Templates
What governance frameworks are taught to ensure ethical AI adoption?

Speed must not compromise security. We address critical data privacy concerns, teaching participants exactly what proprietary or confidential data must never be fed into open-source LLMs. We provide actionable frameworks to ensure AI usage complies with institutional ethics boards and publisher mandates.

Explore the AI Ethics & Guardrails Policy
Does the FDP cover the synthesis and presentation of research data for stakeholders?

Yes. Translating complex research into digestible stakeholder presentations is a key capability. The final modules focus on utilizing tools like Microsoft Co-pilot to instantly convert research findings into executive-ready slide decks, ensuring high-impact delivery of your core insights.

See How AI Builds Executive Decks
Is this training purely theoretical, or is it hands-on application?

This is a capability enablement program, not a passive seminar. Every module is anchored in real-world application. Participants will log into the tools, execute prompts, map actual literature, and build real data pipelines during the session, ensuring immediate skill transfer to the workplace.