AI Literacy as Disciplinary Practice
One Day Hands-on Workshop & Roundtable
Proposed by GreenAI Services Pvt. Ltd. · DPIIT-Recognised AI Startup · Kolkata · Mumbai
Large Language Models produce fluent, confident, well-structured text. This fluency is precisely what makes them dangerous in academic settings. A paragraph that reads well is not necessarily a paragraph that reasons well — and this distinction is the central intellectual challenge that generative AI poses to every department in the university.
Jadavpur University's intellectual traditions — spanning subaltern historiography, postcolonial literary criticism, advanced materials science, and computational linguistics — demand a form of AI engagement that no generic workshop can provide.
This programme equips faculty to critically evaluate what AI produces, identify where it fails within their own discipline, and make sovereign decisions about when, whether, and under what conditions to deploy it in research, teaching, and institutional governance.
Mandates integration of emerging technologies into faculty development with disciplinary rigour and verified outcomes.
Calls for critical AI literacy — the capacity to evaluate, interrogate, and responsibly deploy AI, not mere tool familiarity.
Not merely referenced but taught — a dedicated session on compliance obligations that directly affect academic AI use.
Recommends experiential, project-based learning embodied throughout the day's facilitated sessions.
Three parallel tracks — STEM & Formal Sciences, Social Sciences & Economics, and Humanities, Arts & Philosophy — within a single cohort. Every hands-on exercise, prompt template, and critique methodology is calibrated to the epistemic norms of each field.
Most AI workshops foreground prompt engineering — how to get better outputs. This programme inverts that emphasis. The core competence developed is critical verification: the ability to identify what an AI has misrepresented, whose knowledge it has erased, and what epistemic assumptions it has silently imported.
Data protection is not relegated to a compliance footnote. A dedicated session unpacks the DPDP Act as it directly affects faculty who use AI in research, teaching, and student assessment — covering consent architectures, data fiduciary obligations, and cross-border data flows to LLM providers.
A dedicated session introduces faculty to autonomous, multi-step AI workflows that can execute complex academic tasks — literature synthesis across 50 papers, systematic data extraction, multi-source fact-checking — without requiring a single line of code.
The workshop produces a tangible institutional output — discipline-specific AI use statements that feed into Jadavpur University's emerging AI Policy Repository, evolved within the university itself by its own faculty.
All participants attend the same plenary sessions and panel discussions, but hands-on exercises are customised by track. This respects the epistemic diversity of JU's faculty without fragmenting shared intellectual experience.
Physics · Mathematics · Computer Science · Engineering
Economics · Political Science · Sociology · Cognitive Science · Law
Bengali Literature · History · Philosophy · Fine Arts · Linguistics
Conducted entirely live and on campus. Click any session to expand details.
Most AI workshops treat data privacy as a slide at the end. This programme treats it as a core competence. The DPDP Act 2023 has fundamentally altered the legal landscape within which any AI-using institution operates — and yet most faculty remain unaware of the obligations it imposes on their everyday workflows.
Session III covers consent architectures, data fiduciary obligations, cross-border data flow analysis, the distinction between data processors and data fiduciaries in AI contexts, institutional DPA requirements, and practical compliance checklists.
Consider these scenarios — each triggers DPDP obligations that most faculty are unaware of:
Those assignments contain personal data. The upload constitutes data processing; the LLM provider becomes a data processor. Has informed consent been obtained? Is there a Data Processing Agreement in place?
If the transcripts contain identifiable information, the researcher has transferred personal data to a third-party processor without the data principal's specific consent for AI processing.
Even anonymised responses can constitute personal data if linkable to individuals through small cohort sizes or distinctive phrasing within a department.
Unlike typical workshops that end when participants leave the room, this programme provides substantive, enduring value beyond the event day.
A comprehensive, discipline-aware reference volume — designed for faculty in Indian universities. Not a generic how-to guide; a structured intellectual resource that bridges epistemological critique with practical methodology.
A unified, DPDP-compliant interface integrating multiple frontier LLMs under a single academic login. Compare outputs across models — making bias, variation, and failure modes visible in ways no single-model experience can.
Complimentary token allocation included · Isolated per-participant data vaults · Full DPDP 2023 compliance · On-premise deployment option available
Upon completion, participants will have demonstrated the ability to: