Dr. Mohd Naved AI in Education and Research

Speaking

Speaking and Academic Sessions

Invited academic lectures, faculty development programs, research scholar workshops, and institutional sessions on AI, education, research, analytics, and responsible academic use.

Academic Bio

Dr. Mohd Naved is an academic and author working at the intersection of artificial intelligence, business analytics, education, research methods, and responsible academic use of emerging technologies. His invited sessions are usually designed for faculty members, students, research scholars, academic leaders, department teams, and institutions preparing for AI-enabled academic work.

The usual emphasis is academic adoption rather than software demonstration: what faculty should permit, what students should disclose, how departments can redesign assessment, how research scholars can protect originality, and how institutions can frame practical AI readiness.

Common Academic Session Areas

  • Generative AI in higher education
  • Agentic AI and future knowledge work
  • AI for research scholars
  • Responsible AI, assessment, disclosure, and academic integrity
  • AI-enabled teaching and faculty development
  • Business analytics, intelligent systems, and decision support

Formats

  • Keynote address
  • Invited lecture
  • Faculty development program
  • Research scholar workshop
  • Conference panel or session
  • Institutional AI readiness session

Session Design

A session can be framed as a broad invited lecture, a practical faculty workshop, a research scholar clinic, or a leadership discussion. The design usually depends on the audience, expected level of AI familiarity, institutional policy context, and whether participants need conceptual clarity, templates, or hands-on academic workflows.

  • Conceptual orientation for mixed academic audiences
  • Hands-on faculty practice for teaching and assessment
  • Research workflow support for scholars and supervisors
  • Policy and governance discussion for institutional teams

Typical Learning Outcomes

Academic sessions are designed so participants leave with language, examples, and decisions they can use after the event. The exact outcomes depend on the audience, but most sessions combine conceptual clarity with practical academic judgment.

  • Faculty can identify where AI may support preparation, feedback, and assessment without weakening learning.
  • Research scholars can distinguish useful AI assistance from shortcuts that damage originality or citation discipline.
  • Departments can begin framing disclosure rules, classroom policy, assessment redesign, and tool-use boundaries.
  • Academic leaders can discuss readiness, governance, privacy, quality assurance, and faculty development.

Common Session Pathways

  1. Generative AI in Higher Education

    A broad academic session for faculty, students, and institutional audiences who need a structured introduction to responsible academic adoption.

  2. AI for Research Scholars

    A research-focused session on literature review, academic writing, verification, disclosure, and publication-oriented workflows.

  3. AI Policy and Governance Workshop

    A working session for departments and institutions preparing practical AI-use guidance, policy clauses, and review processes.

Session Inquiry

Please share the institution, audience, proposed format, date, duration, delivery mode, participant profile, and the academic outcome expected from the session. If the session is part of an FDP, conference, orientation program, or institutional policy initiative, include that context as well.