- Salary
- $18+
- Location
- Penn State University Park, United States of America
- Workplace
- Remote, Hybrid, Onsite
- Type
- Part-time
- Department
- Education
- Source
- Workday
Description
APPLICATION INSTRUCTIONS:
CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.
CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants.
Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.
JOB DESCRIPTION AND POSITION REQUIREMENTS
We are seeking a part time research assistant to join an innovative project at the intersection of artificial intelligence and education. This project, led by Dr. Mahfuza Farooque in the School of EECS at Penn State, focuses on developing a personalized recommendation engine for microlearning content. The RA will play a key role in leveraging student performance data to deliver tailored learning experiences. This position is a unique opportunity to contribute to cutting-edge research that can enhance student learning outcomes. Qualified applicants may include students or other individuals with relevant skills and interest in AI, data analysis, and educational technologies.
Position Details
- Anticipated Start Date: 10th April
- End Date: 30th June
- Weekly Hours: Maximum 15 hours per week per student
- Compensation: $18 per hour
- Location: Flexible (in-person at University Park, remote, or hybrid arrangement)
The research assistant will be involved in various aspects of the project’s development and evaluation. Key responsibilities include:
- Data Analysis: Assist in collecting and analyzing student performance data to inform the recommendation engine.
- Algorithm Development: Contribute to designing, prototyping, and testing algorithmic models for personalized content recommendations.
- System Evaluation: Support the evaluation of the recommendation engine’s effectiveness (e.g. running experiments or user studies to assess learning outcomes).
- Documentation: Help document the system design, development process, and experimental results for project reports or publications.
- Collaboration: Work closely with the project lead and team, participating in regular meetings and brainstorming sessions.
This role offers hands-on experience in AI and data-driven education technology. The RA will have the flexibility to work on-site or remotely as needed and will gain valuable experience on an interdisciplinary project that bridges computer science and learning science.
Preferred Qualifications
- Technical Skills: Familiarity with artificial intelligence/machine learning techniques and data analysis. Experience with programming (Python or similar) and handling educational data is a plus.
- Other Skills: Self-motivated with good communication skills. Ability to work independently and collaboratively in an interdisciplinary team.
Application Instructions
To apply, please submit a resume and a cover letter detailing your background, relevant experience, and interest in this project, in WorkDay. Your cover letter should briefly explain how your skills align with the project and what you hope to accomplish in this role.
For questions, you may contact Dr. Mahfuza Farooque at [email protected] or Dr. Suman Saha at [email protected]. We look forward to your application and the prospect of working together on this exciting AI in Education initiative!
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
