Entry Requirements for AI For Academic & Applied Research
Computer literacy is essential.
Duration: 1 Months
Delivery Method: Both Online & Physical
Fee Structure for AI For Academic & Applied Research
| Full Course Fees | |
|---|---|
| Registration Fee | KES 0.00 ($ 0.00) |
| Certification Fee | KES 0.00 ($ 0.00) |
| Administration Fee | KES 0.00 ($ 0.00) |
| Internal Exam Fee | KES 0.00 ($ 0.00) |
| External Exam Fee | KES 0.00 ($ 0.00) |
| Examining Body Membership Fee * | KES 0.00 ($ 0.00) |
| Tuition Fee | KES 0.00 ($ 0.00) |
| Fees Totals | KES 0.00 ($ 0.00) |
| * Examining Body Membership Fee may be payable through us or directly to the Examining Body | |
Course Requirements for AI For Academic & Applied Research
Practical Requirements for AI For Academic & Applied Research (where applicable)
Course Units/Overview for AI For Academic & Applied Research
Lesson 1: AI in Research Workflows
- Research lifecycle overview
Lesson 2: Topic Development & Research Questions
- AI-assisted ideation
Lesson 3: Literature Review with AI
- Summarization & synthesis
Lesson 4: Data Collection & Organization
- Surveys, interviews, datasets
Lesson 5: AI for Data Analysis (Intro)
- Qualitative & quantitative basics
Lesson 6: Academic Writing & Structuring
- Proposals, reports, theses
Lesson 7: Referencing & Integrity
- Avoiding hallucinations
Lesson 8: Research Brief Project
- Policy or academic research note
Course Description for AI For Academic & Applied Research
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Covers AI and data-driven tools across the full research lifecycle: problem formulation, literature review, data collection, analysis, interpretation, and dissemination.
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Supports both academic research (theses, dissertations, journals) and applied research (policy, market, institutional, and industry studies).
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Introduces AI-assisted research methods for faster, deeper, and more rigorous inquiry.
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Applies AI to intelligent literature discovery and reference management.
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Uses AI for qualitative and quantitative data analysis, including coding automation and pattern detection.
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Optimizes research workflows to improve efficiency, consistency, and reproducibility.
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Emphasizes AI as an augmentation tool, strengthening—not replacing—critical thinking and scholarly judgment.
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Embeds research integrity and ethics at the core of AI-enabled research practice.
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Examines issues of authorship, originality, bias, transparency, data privacy, and responsible AI use.
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Aligns AI research practices with institutional policies and global research ethics standards.
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Builds capacity to critically evaluate AI-generated outputs before adoption or publication.
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Prepares learners to design and execute AI-supported research projects responsibly.
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Strengthens research quality, productivity, and real-world impact.
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Suitable for students, researchers, educators, analysts, and professionals operating in academic and applied research environments.
Course Instructor(s) for AI For Academic & Applied Research
TBA
Examining Body for AI For Academic & Applied Research
FINSTOCK EVARSITY COLLEGE
FAQs for AI For Academic & Applied Research
1. What is this course about?
This course focuses on how Artificial Intelligence (AI) and data-driven tools can be used to enhance academic and applied research across the full research lifecycle, from idea development and literature review to data analysis and dissemination.
2. Who is this course for?
The course is designed for students, researchers, educators, analysts, policy professionals, and practitioners who want to improve research quality, efficiency, and impact using AI responsibly.
3. Do I need prior experience with AI or data science?
No prior AI or data science experience is required. The course is designed for beginners and focuses on practical, guided use of AI tools within research contexts.
4. What skills will I gain from this course?
You will learn how to use AI for literature discovery, reference management, qualitative and quantitative analysis, research workflow optimization, and ethical evaluation of AI-generated outputs.
5. Does the course cover research ethics?
Yes. A strong emphasis is placed on research integrity, authorship, originality, bias, data privacy, and responsible use of generative AI in academic and professional research.
6. How is the course delivered?
The course is delivered through face-to-face sessions, online learning (Google Meet), and self-paced study, depending on institutional arrangements.
7. How will I be assessed?
Assessment includes continuous assessments and a final examination, focusing on both practical application and conceptual understanding.
8. Will this course help with thesis or project work?
Yes. The course is specifically designed to support thesis writing, dissertations, policy research, and professional research projects using AI-enabled methods.
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