ABET Program Evaluator (PEV)
Service as an ABET Program Evaluator reflects sustained engagement with engineering program review, student outcomes, continuous improvement, and accreditation quality culture.
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Electrical Engineering · Clean Energy · AI-Era Education
Adel Gastli
Professor of Electrical Engineering, ABET Program Evaluator, Stanford/Elsevier top 2% scientist recognition holder, and education innovator working at the intersection of sustainable energy systems, engineering education, free knowledge sharing, and the future of human thinking in an AI-enabled world.
ABET Program Evaluator (PEV)Engineering program accreditation and outcomes-based quality assurance.
Stanford/Elsevier Top 2% RecognitionCitation-based research visibility in public Stanford/Elsevier-related listings.
Free Knowledge SharingPublic YouTube course materials and access for all.
4Countries of academic and leadership contribution
EnergyRenewables, smart grids, EVs, PV systems, and drives
AIThought leadership on education, thinking, and human development
ABET PEV · Top 2% RecognitionResearch, teaching, accreditation quality, and free public learning.
Signature Credentials
Research excellence, accreditation leadership, and free knowledge sharing.
Three pillars define this professional identity: internationally visible engineering research, quality-assurance service through program accreditation, and a commitment to open educational access.
Stanford/Elsevier Top 2% Scientist Recognition
Public sources report that Adel Gastli has been recognized in Stanford University-related top 2% scientist listings. Qatar University recognition was also reported by Gulf Times, while the official Stanford/Elsevier database explains the citation-indicator methodology.
Free knowledge sharing and access for all
Digital materials for several electrical-engineering courses have been developed and made publicly available through YouTube, expressing a clear belief that high-quality knowledge should be accessible beyond classroom boundaries.
Areas of Expertise
Engineering knowledge shaped for a rapidly changing world.
My work connects rigorous electrical engineering scholarship with clean-energy transformation, quality assurance, open learning, and responsible AI-era education.
Professional Engagements
Field engagement in renewable energy and quality assurance.
Selected photographs connect the portfolio narrative to renewable-energy site visits, quality-assurance leadership, and international professional participation.
Featured Thought Leadership
Protecting human thinking across the AI lifespan.
The AI education white paper examines how schools and universities can use artificial intelligence without weakening the cognitive, social, and ethical capacities that education is meant to cultivate.
Intentional Friction
Learning should preserve effort, reflection, and productive struggle rather than outsourcing thinking too early.
Developmental Scaffolding
AI use should vary across childhood, adolescence, higher education, and professional formation.
Ethical Co-Creation
The goal is not to reject AI, but to position it as a tool that strengthens judgment, creativity, and responsibility.
Professional Journey
Read the Biography
Research, leadership, and education across regions.
My professional journey connects research, teaching, leadership, and institutional development across Qatar, Oman, Japan, and Tunisia. This website brings together my biography, research interests, public teaching videos, selected projects, and current reflections on engineering education and AI.
Research
Teaching
Quality
Innovation
Media & Learning
Videos, resources, and public learning.
The site will host and organize educational videos, engineering learning resources, and selected professional materials so students, colleagues, and collaborators can access them through a clean, modern interface.
Explore Teaching VideosAdvancing clean energy, engineering education, and responsible AI learning.
I welcome professional collaboration around sustainable energy systems, engineering education quality, academic leadership, and the responsible integration of AI into learning environments.
Engagements
Professional Engagements
Engagements
Selected field visits, quality-assurance events, and public academic participation connecting engineering research with institutional development and AI-era education.
Photo Stories
Field engagement in renewable energy and higher-education quality.
These photographs document selected public-facing moments from renewable-energy engagement and quality-assurance participation.
Themes
Engagements that connect technology, education, and public service.
Renewable-energy infrastructure
Site visits and professional exposure to solar PV, concentrated solar power, and wind-energy technology connect research themes to practical deployment.
Academic standards and assurance
Quality-assurance forums and international panels support dialogue about evidence, assessment, program quality, and institutional improvement.
Responsible AI in higher education
AI-era education requires universities to preserve human judgment, verification, and professional accountability while adopting useful tools.
Contact
Contact
Contact
Professional communication is welcome for academic collaboration, research, invited talks, workshops, quality assurance, accreditation, and responsible AI in education.
Professional Inquiries
Please include enough context to make collaboration easy to assess.
For academic collaboration, invited talks, workshops, consulting, program review, or education-focused events, please include the purpose of the inquiry, institutional affiliation, proposed timeline, and any relevant links or attachments.
Direct contact
Professional base
Doha, Qatar, with international academic and professional engagement.
Areas of interest
Energy efficiency, renewable energy, smart grids, electric vehicles, engineering education, quality assurance, accreditation, and AI in education.
Collaboration formats
Academic and research collaboration
Research exchange, project discussion, technical seminars, publication-related collaboration, and clean-energy or engineering-education initiatives.
Quality assurance and education events
Invited talks, workshops, program review, accreditation-related discussion, AI in higher education, and outcomes-based quality culture.
White Paper
White Paper
Education in the AI Era
A developmental framework for using artificial intelligence in education without weakening human thinking, ethical judgment, and learner agency.
Core Argument
AI should reduce unnecessary barriers while preserving meaningful intellectual effort.
The white paper argues that AI should not be used in the same way across all stages of learning. Its role should evolve according to the learner’s cognitive, emotional, social, and professional maturity.
AI as adult co-pilot
AI should remain mainly in the background, supporting teachers and parents as they create richer human-to-human learning interactions.
Adaptive and Socratic support
AI can personalize practice and feedback, but it must include guardrails that preserve reasoning, explanation, and productive struggle.
Professional discernment
Students should learn to critique, verify, document, and defend AI-supported work in authentic professional and disciplinary contexts.
Central Principle
Intentional friction protects learning.
AI should not make learning frictionless. Good education uses AI to remove unnecessary barriers while preserving the intellectual effort through which learners build judgment, understanding, and agency.
Developmental Pathway
From protection to guided use to professional co-creation.
Protect relational learning in early childhood
Young children need embodied interaction, language-rich relationships, play, and adult mediation more than direct AI companionship.
Use AI for guided practice in K–12 education
AI can help teachers differentiate practice and feedback while requiring students to explain their reasoning and confront misunderstanding.
Teach transparent AI collaboration in higher education
University students should learn how to use AI ethically, verify outputs, disclose assistance, and maintain professional accountability.
Related media
The videos below introduce the white paper’s themes and the broader question of how education should adapt to generative AI.
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