AI literacy & inquiry
How learners frame questions, compare models, and check the evidence behind a fluent answer.
Explore the questions →Educator & AI practitioner · Lebanon
Elias Atik
Exploring how AI changes the way we learn.
I teach AI at the Lebanese American University and co-lead Webspot. My focus is the connection between practical AI systems, effective teaching, and the critical judgment needed to use AI in education and research.
Lebanese American UniversityPart-time AI faculty · Byblos
WebspotCo-founder & Co-CEO · Applied AI & training
Three connected areas
Where my work comes together
Teaching & assessment
Classroom practice and faculty development built around clear instructions, meaningful feedback, and human judgment.
See the education work →Applied AI at Webspot
AI systems and professional training provide practical context for questions about reliability, adoption, and skills.
Connect practice to research →
From professional practice to the classroom
The questions travel both ways.
Building and teaching with AI raises many of the same questions: What context is missing? How do we judge an output? When should a person step in? I bring those questions into my course design and my interests in AI-supported learning.
My work at Webspot →Student collaboration
Students working with me
The students collaborating with me on my AI education work.
Alec AgochianStudent collaborator
Mario BoyajianStudent collaborator
Sean Pio KachakjianStudent collaborator
Elio KiwanStudent collaborator
Research interests & working questions
Better learning starts
with better questions.
My interests grow out of teaching AI, developing faculty workshops, and building practical systems. I am particularly interested in how people learn to direct AI, evaluate its outputs, and retain responsibility for the result.
01
Prompting & reproducibility
What makes an instruction reliably useful across tasks and models?
Focus
Task clarity, relevant examples, context, constraints, and clarification. The workshop’s TRROC+ framework makes these choices explicit.
Possible comparison
Use the same classroom task with a vague prompt and a structured prompt; compare relevance, evidence use, and consistency.
02
Feedback & judgment
When does AI feedback help an educator make a better assessment?
Focus
Rubric alignment, evidence-linked observations, calibration with exemplars, and the difference between feedback and a grading decision.
Possible comparison
Compare model feedback with a lecturer’s review of a sample or de-identified submission, examining omissions and unsupported judgments.
03
Source-grounded inquiry
How can AI assist research without weakening verification habits?
Focus
Source selection, citation checking, uncertainty, and the distinction between a plausible synthesis and a supported claim.
Possible comparison
Contrast an unconstrained answer with one grounded in selected readings; trace the claims back to the underlying material.
A practical evaluation approach
Keep the task, sources, and judgment visible.
This sequence connects the classroom exercises and the faculty workshop design. It also offers a starting point for exploring the questions above.
01 / DEFINEA real learning task
State the learning objective and the expected output.
02 / GROUNDShared sources & criteria
Provide readings, an assignment brief, or a rubric.
03 / COMPAREPrompts & model outputs
Examine accuracy, relevance, and evidence use.
04 / REVIEWEducator judgment
Check findings, discuss limits, and refine the task.
These are research interests and proposed comparisons, informed by teaching and curriculum design. They are not presented as completed studies or published findings.
Educational foundation
Teaching gives the questions a setting.
LAS 211 includes cross-model comparisons, iterative prompting, no-code projects, and discussion of AI-assisted research. The faculty workshop extends that focus to assessment, feedback, and reusable teaching workflows.
Applied foundation
Practice gives them consequences.
Webspot’s AI systems and professional training bring questions about context, reliability, user understanding, and oversight into practical settings. Those concerns inform the educational questions I want to explore.
Teaching & faculty development
From using AI
to understanding its use.
My education work spans an undergraduate AI course and a faculty workshop scheduled for October 27, 2026, on teaching and assessment. Both combine hands-on practice with critical evaluation.
Prompting the Future
with AI
A liberal arts introduction to AI, generative tools, and their academic, professional, and societal implications. No prior technical experience is required.
- Compare: test the same prompt across models and rank results with a rubric.
- Build: use prompt and context engineering to develop a working no-code web application.
- Question: examine AI-assisted research, misinformation, ethics, and academic integrity.
Based on the LAS 211 course syllabus.
The teaching principle
Make the reasoning
part of the work.
A useful AI activity gives learners something to evaluate: a questionable claim, two different outputs, a draft with missing evidence, or a prompt that needs refinement.
- Before: define the task, audience, context, and criteria.
- During: inspect the output and ask what is missing or unsupported.
- After: revise the work and explain the choices made.
Upcoming faculty workshop · October 27, 2026Teaching & Assessing
Teaching & Assessing
in the Age of AI
Scheduled for
A workshop curriculum I designed for university lecturers, anchored in LAS 212: Communication in the Age of AI, with methods adaptable to writing, humanities, social sciences, design, and research methodology.
Five focused blocks, followed by an open faculty clinic
25 minModels & critical AI literacy
50 minPrompt engineering with TRROC+
40 minAssessment & feedback
40 minAI as a course workspace
20 minEducator toolkit
175 minutes of content + a 5-minute transition = 3 hours; followed by a 60-minute Q&A. Planned program for the October 27, 2026 workshop.
TRROC+: instructions with a purpose
The framework makes task design visible and reusable. A faculty exercise turns a vague request for poster feedback into an instruction grounded in the assignment rubric and evidence requirements.
TTask
RRole
RReferences
OOutput
CContext
CConstraints
+Clarification
Assessment with human oversight
Participants work from an assignment brief and rubric, request observations tied to the actual submission, compare suggestions with lecturer judgment, and review every finding before feedback or marks are issued.
The lecturer retains the final decision. AI-detection scores are treated as non-conclusive signals, and teaching exercises use appropriate sample or de-identified material.
Planned takeaways: a reusable teaching prompt, a rubric-grounded feedback workflow, a draft course workspace, and a practical method for evaluating new tools.
Webspot
Applied practice.
Educational questions.
I am Webspot’s co-founder and Co-CEO. Alongside my teaching, my work involves AI systems, practical workflows, and training people to use AI effectively.
This gives my education work a practical foundation: the challenges of directing a model, understanding its limits, and keeping people accountable for its outputs also matter in classrooms and academic inquiry.
Professional work → education research interests
How the two sides connect
AI systems & workflows
Webspot describes agentic systems that connect models with organizational tools, data, and workflows.
The education connection
What context and source material does an assistant need? How should its output be checked? These questions also shape source-grounded learning and course workspaces.
AI systems at Webspot ↗Practical AI training
Its training combines live demonstrations, applied exercises, prompt writing, and discussion of participants’ own tasks.
The education connection
How do people move from watching a demonstration to using AI competently? This informs my interest in instructional design and transferable AI literacy.
Training activities & speakers ↗AI readiness & adoption
Webspot’s AI audit work examines existing systems, data practices, gaps, and possible integration paths.
The education connection
Adoption depends on more than selecting a tool. Educators also need clear criteria, suitable materials, and an explicit process for human review.
AI audit & consulting ↗Professional practice
Real tasks reveal real limitations.
Working with AI systems and learners makes context, verification, and usability concrete concerns.
Teaching & research interests
Education turns them into questions.
My course and workshop designs ask how people learn these skills and how educators can evaluate them responsibly.