eResearchPro

Overview

In brief:
A selective, small-cohort program where advanced middle school students build a real clinical data and AI system using professional tools, taught by a PhD Clinical Informatics Director and a veteran K–8 curriculum expert. Instruction is supported by a small instructional team, ensuring a low student-to-instructor ratio and individualized guidance throughout all sessions. By the end of the program, students can explain how hospitals store medical data, analyze it, visualize it, and automate updates, using real professional tools

Admission is selective, with small cohorts designed to support students who are academically curious, highly motivated, and ready for advanced material. All instruction is carefully scaffolded to ensure students are challenged without being overwhelmed.

Students are placed into one of two instructional tracks—Foundations (5th–6th grade) or Deeper Dive (7th–8th grade)—to ensure appropriate pacing while maintaining sustained academic challenge across the full grade span.

Program sites rotate between New York, Boston, Washington DC, Chicago, San Francisco, London, Dubai, Singapore, and Seoul, Korea.

Program Format

• Audience: Advanced students in grades 5th–8th
• Schedule: Monday - Friday from 5:00 p.m. to 7:30 p.m.; Saturday 8:00 a.m. to 12:00 p.m.
• Format: In-person, small cohort instruction
• Class Size: Maximum 20 students per cohort for individual guidance
• Location: Professional executive classroom environment   NY Academic Venue
• Tuition: $3220.00 per student
• Technology: Microsoft Windows laptops are provided for the course. Students may bring their own Windows laptops if preferred, subject to a brief compatibility check to ensure required software will run correctly.

What Students Will Learn

0. Zero Day: Pre-Interviews

The senior instructors will interview each student prior to the class week using ZOOM. The purpose is to understand each students level of learning, to determine the computer they will use, and to download any software required for the course

1. Day 1: Introduction to Artificial Intelligence: A Foundational Seminar (Monday)

Core Topics Covered (Graduate-Style Overview)

During this lecture, students will engage with the following core concepts, presented in a structured, university-level framework:

• Foundations of Artificial Intelligence
  Definition of artificial intelligence and its major subfields, including machine learning, deep learning, and generative AI, with emphasis on how modern AI systems differ from traditional rule-based software.
• Neural Networks and Large Language Models
  Conceptual overview of neural networks as pattern-recognition systems inspired by biological neurons, and how large language models process text using tokens and probability-based prediction rather than understanding or intent.
• How ChatGPT Works (Conceptually)
  Explanation of how models such as GPT-3.5 are trained on massive text corpora, how they predict the next most likely word in a sequence, and why confidence does not imply correctness.
• Prompt Engineering as a Technical Skill
  Introduction to prompt engineering as an emerging discipline focused on structuring inputs clearly and precisely to improve AI output quality, reproducibility, and relevance.
• AI Hallucinations and Model Limitations
  Examination of why AI systems can generate incorrect or fabricated information, the risks this poses in academic and professional settings, and strategies for detection and mitigation.
• The Role of Domain Knowledge
  Emphasis on subject-matter expertise as a safeguard against AI error, reinforcing that AI outputs must always be evaluated against known principles, expected ranges, and logical constraints.
• Ethics, Privacy, and Responsible Use
  Discussion of appropriate AI use in educational contexts, including academic honesty, data privacy, respectful communication, and the long-term implications of over-reliance on automated systems.

This session sets the intellectual tone for the program by establishing AI as a tool for augmentation, not substitution, and by modeling the analytical discipline expected in advanced academic and professional environments. The same framework is subsequently translated into age-appropriate language and activities for younger and older student cohorts, without diluting the underlying rigor.

2. Real Database Foundations (Tuesday)

In this foundational session, students are introduced to databases as the formal systems that underpin all serious data analysis, statistics, and how artificial intelligence can support their understanding.  Rather than treating data as simple spreadsheets, students learn how professionals design databases to store information accurately, prevent errors, and support reliable analysis. The session emphasizes disciplined thinking about data structure, integrity, and accountability before any analysis occurs. Students are guided through the creation of a database environment, the design of structured tables, and the controlled manipulation of data using professional workflows. This seminar mirrors how databases are taught in university-level informatics and biostatistics programs, translated into clear, age-appropriate instruction without reducing intellectual rigor.

• Understand the difference between files, spreadsheets, and relational databases

• Learn how databases, tables, rows, and columns represent structured information

• Create a database and design tables with appropriate fields and data types

• Identify the role of primary keys and why unique records matter

• Perform core database operations (Create, Read, Update, Delete) as controlled actions

• Learn why databases enforce rules to prevent incorrect or inconsistent data

• Understand how clean database design supports statistics, dashboards, and AI models

• Prepare tables that will later be populated with realistic data for analysis

3. Introduction to Statistical Analysis Using PSPP (SPSS-Style Software)  (Wednesday)

In this session, students are introduced to statistical thinking through PSPP, a professional-grade statistical software environment modeled after SPSS and widely used in academic and research settings and how artificial intelligence can support their understanding.  The focus is not on memorizing formulas, but on understanding how structured data are explored, summarized, and interpreted using statistical tools. Students learn how analysts move from raw data to meaningful insights by organizing variables, running an analysis, and visualizing results. Emphasis is placed on how statistics support decision-making in medicine, public health, and research, and why correct interpretation matters more than simply producing numbers. This session mirrors how statistical software is introduced in university biostatistics and public health informatics courses, translated into clear, age-appropriate instruction while maintaining conceptual rigor.

• Understand what statistical software does and why professionals use tools like SAS, SPSS and PSPP
• Learn the structure of a statistical dataset, including variables, cases, and measurement types
• Import and view data prepared from a database table
• Define variable properties such as labels, value codes, and data types
• Run descriptive statistics to summarize data (counts, averages, ranges)
• Create basic statistical tables and charts to visualize patterns in data
• Understand the difference between describing data and making conclusions
• Learn why data must be clean and well-structured before any statistical analysis
• Prepare for later modules involving probability, hypothesis testing, and interpretation

Why This Session Matters

This session intentionally aligns with the statistical modules in a graduate biostatistics curriculum, where students first learn to understand their data before testing hypotheses . By introducing a statistical immediately after database foundations, students experience the same analytical pipeline used in higher education and research: database → statistical software → interpretation.

4. Excel Storyboard & Visualization(Thursday)

• Import data from the Microsoft SQL database into Excel and automatically refresh it using Automation & Scheduling
• Create tables, slicers, pivot tables, and charts
• Design a data storyboard that explains a patient or population health story
• See how charts and graphs can visually highlight trends, errors, and outliers before formal statistics are performed
• Build an interactive dashboard sheet that updates when slicers are clicked
• Watch how changing the data in the database automatically changes the storyboard

5. Introduction to Data Acquisition, Automation, and Real-Time Updating Systems (Friday)

In this session, students are introduced to the collection of real-world data outside a computer and to their integration into professional data systems used in medicine and research and how artificial intelligence can support their understanding.   Using a Bluetooth-enabled wearable device paired with a smartphone, students learn how external data, such as sensor or health-related measurements, can be securely transmitted to a Windows-based environment. A custom C# application is used to receive and prepare incoming data, which is then inserted into a structured database. Students are further introduced to automation concepts by observing how scheduled processes can run without human intervention, continuously update databases, and drive downstream changes in analytical tools such as Excel dashboards. This session mirrors how hospitals, research labs, and monitoring systems collect, automate, and analyze incoming medical and physiological data in real time, and translates these processes into explicit, age-appropriate instruction while maintaining professional rigor.

• Understand how medical and sensor data can be collected from external devices using Bluetooth
• Learn how wearable devices and smartphones act as data collection tools in health and research settings
• Observe how a Windows-based C# application receives, formats, and prepares incoming data
• Understand how external data is validated before being accepted into a database
• Learn how scheduled tasks allow systems to run automatically without manual input
• Use Windows Task Scheduler to trigger data updates at defined intervals
• Understand how command-line SQL scripts can insert and update records in a database
• See how new data automatically changes database contents without reprogramming
• Observe how updated database values drive real-time changes in Excel dashboards
• Understand the complete professional pipeline: device → application → database → automation → analytics

Day 6: Weekly Review, System Integration, and Project Finalization (Saturday)

This concluding session functions as a capstone seminar in which students consolidate and operationalize the full set of concepts introduced throughout the week. Rather than introducing new tools, the focus is on integration, validation, and explanation. Students revisit artificial intelligence, database design, statistical analysis, visualization, data acquisition, and automation as a single, continuous professional workflow. Emphasis is placed on confirming that each component functions correctly, that outputs are logically and statistically defensible, and that students can clearly articulate how data move through the system and why design decisions were made. The session reinforces the expectation that technical systems must be reliable, interpretable, and ethically grounded, with human judgment serving as the final authority over all automated outputs.

Core Topics Covered (Graduate-Style Overview)

During this capstone session, students engage in a structured review and finalization process aligned with university-level informatics and biostatistics practice:

• Integrated Review of the Full Analytical Pipeline
  Reexamination of the complete workflow—artificial intelligence concepts, structured database design, statistical analysis, visualization, and automated data updating—as a unified system rather than isolated tools.
• Database and Data Integrity Validation
  Verification of table structure, field definitions, data types, primary keys, and record uniqueness, with emphasis on identifying missing values, inconsistencies, and out-of-range data before final analysis.
• End-to-End System Integration Testing
  Confirmation that the full pipeline operates correctly from external data collection through application processing, database insertion, scheduled automation, and downstream analytics and visualization.
• Statistical Output Review and Interpretation
  Re-running descriptive analyses in PSPP, confirming that statistical summaries align with expectations, and ensuring that interpretations accurately reflect the data without overstatement or misrepresentation.
• Visualization and Storyboard Refinement
  Finalization of Excel-based dashboards and storyboards to clearly communicate patterns, trends, and data quality issues, reinforcing visualization as an interpretive and diagnostic tool rather than decoration.
• Automation Verification and Reliability Assessment
  Review of Windows Task Scheduler processes and command-line SQL execution to ensure updates occur automatically, consistently, and without manual intervention.
• Professional Explanation and Communication of Results
  Preparation of a concise final presentation in which students explain system design, data flow, results, limitations, and ethical considerations using clear, evidence-based reasoning.

This session completes the program by requiring students to demonstrate not only technical execution, but also analytical discipline, interpretive accuracy, and professional accountability. By finalizing and explaining a fully integrated system, students experience the same expectations placed on practitioners in academic research, healthcare informatics, and data-driven decision-making environments.

Conceptual Emphasis for Parents

This session reinforces the idea that data do not magically appear for analysis. Students observe firsthand how raw data is captured, transmitted, validated, scheduled, stored, and ultimately visualized—mirroring the automated workflows used in clinical monitoring systems, research studies, and modern health informatics platforms.

Two Learning Tracks