Library Instruction & Training


We offer training, workshop, and sessions designed to empower the university community with the skills needed to succeed academically and professionally.
  • Embedded
    Requested by professors as part of their classes. These can be one-shot sessions or involve all or selected modules as part of the Information Literacy (IL) Program
  • Open Sessions
    Organized by the Library and open to all to provide relevant information in support of the academic community's research and information needs.
  • Partners' Training
    Training on databases, resources, and services in partnership with publishers, information providers. NU experts in the Schools, IREC and other departments.
  • Orientation
    Offered by the Library at the start of every semester, including library tours. This includes introduction to the services and available resources to new students and teaching members.
Training Calendar
Information Literacy Modules
Research Intelligence & Scholarly Communication Practice
Academic Publishing Support & Research Integrity
AI Literacy at NU
Request a Session
Information Literacy Program (NU LILY)
The Information Literacy Program is offered to foster student success, cultivate active lifelong learners and critical thinkers within the community. The program aims to help students enhance their skills in searching, accessing, evaluating, applying, and acknowledging the right information.
Module 1
Establishing the Need for Information
Module 2
Searching Strategy
Module 3
Evaluate Information
Module 4
Use & Dissemination of Information
Research Intelligence & Scholarly Communication Practice
A series of training sessions designed to strengthen researchers’ core research skills and their understanding of scholarly practices. The program supports faculty members, graduate students, and researchers by developing competencies in research data literacy and responsible data practices, research evaluation, scholarly communication, research ethics, open science, and responsible research conduct.
Learning Outcomes
  • Identify and apply principles of research data literacy and responsible data management throughout the research lifecycle
  • Develop and evaluate data management plans (DMPs) in line with funder and institutional requirements
  • Use appropriate tools and platforms for data organization, documentation, storage, and sharing
  • Critically assess research outputs, journals, and metrics to support responsible research evaluation
  • Apply principles of scholarly communication, including open access publishing and copyright awareness
  • Demonstrate understanding of research ethics, open science practices, and responsible research conduct
Academic Publishing Support and Research Integrity
The training program designed as a mandatory component for members of the Post-Award Operations team and Project Administrators at the Office of Research Policy and Analysis (NURA). The training is specifically important for the managers to guide and support grant applicants to meet NU requirements in terms of publishing research outputs.
  • Duration
    • one month long - twice a week (34 hours)
    • one week long - daily (34 hours)
    • two weeks long - three times a week (18 hours)
  • Assessment
    • Pre-test for needs assessment
    • Post-test and knowledge checking
    • Practical hands-on with tools
  • Facilitators
    The Teaching & Learning Support Office
    The Research Support Office
  • Format
    • In-person instruction
    • Moodle https://moodle.nu.edu.kz/course/section.php?id=275797
Program
Module 1. Introduction to Academic Publishing Support and Research Integrity
  • Welcome and Overview of the Training Program
  • Importance of post-Award and pre-award operations of the project administration in research publication
  • Brief overview of NU’s research policies and strategic goals related to publication
  • How supporting responsible publication helps NU’s reputation and grant success
  • Setting expectations for the course
  • Pre-test (needs assessment)
Module 2. Identifying High-Impact Journals for Your Research Publication
  • Understanding journal Impact factor
  • Understanding CiteScore
  • Understanding Quartiles and Percentiles
  • Understanding h-index
  • Responsible Use of Research Metrics
Module 3. Choosing the Right Journal for Publication
  • Why journals selection matters & key strategies
  • Relevant metrics for publication
  • Tools and databases: Scopus & Web of Science
  • Tools and databases: SJR & JCR
  • Tools and databases: suggestions tools
  • Kazakhstani journals indexed in WoS & Scopus
  • Aligning research with journal - authors guidelines
  • Types of articles (review, original research, case study, data article)
  • RDM requirements for publishing articles
  • Author service
Module 4. Publishing Articles
  • Journal publishing process
  • Steps in manuscript submission
  • Quality assurance (understanding peer-review process and editorial expectations (including article retraction)
  • Publishing models (traditional, open, self-publishing, hybrid)
  • Benefits of Open access
  • Predatory journals: How to spot differences?
  • Understanding APCs (Article Processing Charges) and funding support
  • Authors’ rights, copyright & licensing in Open Science
Module 5. Ensuring Responsible Publishing
  • Publication ethics & responsibility
  • Responsible use of AI in drafting publications and grant applications
  • Good practice guidelines for AI use in research
Module 6. Ensuring Future Readiness
  • Summary of key learning points
  • Common mistakes to avoid when supporting grant applicants
  • Encouragement to maintain ongoing learning
AI Literacy at NU Library
The AI literacy program is part of the Information literacy program for critical learners. The IL principles serve a foundation for AI literacy. The program aims to develop AI literacy skills, including the ability to identify needs, effectively prompt and interact with AI systems, critically evaluate AI-generated information, and apply it responsibly and ethically.
  • Duration
    • Weekly workshops and training
    • 30 min to 2 h
  • Assessment
    • Pre-test for needs assessment
    • Post-test and knowledge checking
    • Practical hands-on with tools
  • Facilitators
  • Format
    • In-person
    • Online
Program
Introductory Level. AI Literacy Basics
  • What Is AI? (Understanding artificial intelligence, machine learning, and neural networks in plain language)
  • How does AI work? Behind the scenes of algorithms (basic concepts of how AI models are trained, including datasets, pattern recognition, and feedback loops)
  • Types of AI in everyday life (exploring AI in search engines, recommendation systems, voice assistants, chatbots, and smart devices)
  • Types of AI in research (exploring AI in search engines including in academic databases)
  • AI hallucinations and biases
  • Prompt engineering
  • Ethical considerations
  • AI and academic integrity at NU
AI Powered Research
  • Efficient searching and application in the subjects fields (Social Sciences and Humanities, Economics, Business, Engineering, Geosciences, Public policy, Sciences, Medicine)
  • Using AI for generating search terms
  • AI assisted literature searching and discovery, research synthesis and summarization, writing and editing support, data analysis and visualization
  • Comparison of traditional literature search and AI powered
  • Advanced prompting (designing complex prompts, iterative refinement, and strategies for using AI as a thinking partner rather than a shortcut.)
  • Evaluating and selecting AI tools (frameworks for comparing AI tools based on purpose, transparency, cost, accessibility, and risk)
  • Critical evaluation of AI outputs
AI in Scholarly Communication
  • Responsibility in AI-mediated scholarly practices
  • Publishers’ policies and acceptance of AI peer-review
  • AI in open scholarship infrastructure (accessibility services, translation, issues of transparency, authorship).
AI in Research Data Management
  • AI-assisted data cleaning, classification, and annotation
  • Automated metadata generation
  • Support for data discovery and integration
  • Quality assurance and anomaly detection
  • Tools for managing large or complex datasets.