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Beginner Level3 Gün

Prompt Engineering for Academics | LLM Strategies for Literature Review, Academic Writing, Grants, Teaching Design & Research Ethics

Use LLMs ethically in academic workflows: literature structuring, drafting, grants, teaching plans & rubrics, reviewer responses—with prompt chaining, role prompts, quality rubrics, and safety controls.

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About This Course

This training transforms the use of LLM in academic production from a "one-off question-and-answer" level into designed and repeatable academic workflows. Participants learn to manage steps such as clarifying the research problem, extracting the conceptual framework, categorizing the literature into themes, generating research questions/hypotheses, comparing methodological options, and constructing the article/thesis framework using a prompt chaining approach.

The second axis is academic writing and teaching production: processes such as rewriting texts with target journal/language/tone/format constraints, highlighting limitations, generating counter-arguments, creating course syllabi and weekly plans, and producing rubrics and assessment materials are structured with format locking, example-based (few-shot) guidance, and multi-turn dialogue design.

The third axis is reliability and ethical discipline: it addresses “source claim rules” that reduce the risk of fabricated citations, verification protocols, human verification, masking for confidential/unpublished content and student data—the minimum data principle—as well as practices for iteratively improving prompts (including A/B trials) by measuring outcomes with rubrics/checklists.

Learning Outcomes

Course Curriculum

1.1 Academic Use-Case Map (Where LLMs Help — Where They Don’t)

  • 1.1.1 Research workflow: problem → literature → method → writing → revision
  • 1.1.2 Teaching workflow: learning outcomes → weekly plan → assessment → feedback
  • 1.1.3 “Assistant vs author” boundary: support tasks vs original scholarly contribution
  • 1.1.4 Suitable tasks: structuring, summarizing, outlining, drafting, rewriting, rubrics
  • 1.1.5 Unsuitable tasks: fabricated sources/data, unjustified certainty, unethical ghostwriting

1.2 How LLMs Behave (Practical Model Behavior)

  • 1.2.1 Probabilistic generation: pattern completion vs “knowing truth”
  • 1.2.2 Hallucination drivers: missing context, ambiguous tasks, misleading prompts
  • 1.2.3 Freshness and domain bias: concept drift across disciplines
  • 1.2.4 Confidence management: ask clarifying questions, abstain when uncertain
  • 1.2.5 Verification discipline: treat outputs as first drafts and validate externally

1.3 Output Discipline (Format + Constraints + Quality Criteria)

  • 1.3.1 Output contract: heading–subheading–bullets / tables / checklists
  • 1.3.2 Constraints: scope (in/out), length, style, audience, language
  • 1.3.3 Quality criteria: conceptual accuracy, coherence, argument strength, clarity
  • 1.3.4 “Negative rules”: specifying what the model must not do
  • 1.3.5 Versioning: v1 (draft) → v2 (improve) → v3 (standardize)

Instructor

Şükrü Yusuf Kaya

Şükrü Yusuf Kaya

AI Consultant & Instructor

Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading the integration of artificial intelligence technologies into the global business landscape. With operations spanning 6 different countries, he bridges the gap between the theoretical boundaries of technology and practical business needs, overseeing end-to-end AI projects in data-critical sectors such as banking, e-commerce, retail, and logistics. Deepening his technical expertise particularly in Generative AI and Large Language Models (LLMs), KAYA ensures that organizations build architectures that shape the future rather than relying on short-term solutions. His visionary approach to transforming complex algorithms and advanced systems into tangible business value aligned with corporate growth targets has positioned him as a sought-after solution partner in the industry. Distinguished by his role as an instructor alongside his consulting and project management career, Şükrü Yusuf KAYA is driven by the motto of "Making AI accessible and applicable for everyone." Through comprehensive training programs designed for a wide spectrum of professionals—from technical teams to C-level executives—he prioritizes increasing organizational AI literacy and establishing a sustainable culture of technological transformation.

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