Environmental health (EH) is the study of physical, chemical, and biological factors in the environment that affect human health. EH data include environmental exposures, health outcomes, and socioeconomic status (SES), which are often place-based or have geographic correlations. This chapter aims to develop students’ spatial literacy skills to address two EH themes—environmental disparities and exposure-health associations—with open online mapping tools. Environmental disparity studies address the disproportionate exposures among populations of low SES and of color. Students will learn to use EJScreen to display maps of emission clusters, pollution levels, and SES, and interpret their relationships. Environmental exposures are associated with multiple adverse health outcomes—e.g., respiratory and cardiovascular diseases. Students will explore the associations by comparing spatial patterns of exposure and disease generated with EJScreen and PLACES, respectively. Students will gain an impression of EH topics and online geospatial tools with class activities and examples.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

To visualize entities that could potentially address the unmet needs of at-risk and disadvantaged community members, a series of activities and exercises geared to participants with little programming or GIS experience were developed using geographic and population information. The learning activities and exercises progress from navigating an existing map to creating a map and uploading to harnessing freely available resources like US Census Bureau data to produce visualizations that link geographic and demographic information. Participants will apply these tools and resources to a specific public health question in a specific region and produce a data-driven report on the answers.By the end of this lesson, students will have combined geographic and demographic data related to public health resources. By processing and uploading data to the map, you have created a custom display where geographic information can be related to other data elements (e.g., population, etc.). This basic workflow is analogous to that used by GIS professionals using sophisticated tools. However, in this activity, we were able to approximate this capability using freely available and relatively user-friendly resources.

The two learning activities outlined in this PowerPoint will aid in developing students’ understanding of spatial epidemiology and the intersectionality of socio-economic and environmental factors. Task 1 involves a short presentation on the software SaTScan covering cluster analysis and including the data types needed for the analysis. Following this, students receive a scenario-based task using a pre-designed hypothetical dataset of the spread of a contagion in the UK. Students input the appropriate text files, developed from contagion datasets, into SaTScan to produce a cluster analysis of unusually high rates of contagion in the region. This task allows for many different manipulations and outputs from the analysis by changing the parameters, such as cluster size and type of analysis in the software. Guidance on the use of SaTScan for the lesson is provided for teachers to help students understand and interpret output using multiple parameter settings. In brief, SaTScan tests the null hypothesis that cases of disease are randomly distributed. Statistical significance suggests that unusual spatial clustering is unlikely to have occurred by chance. This method has been used previously to identify clusters of contagious diseases, such as malaria,23 HIV,24 tuberculosis,25 as well as chronic diseases.26 The output of this session will be a cluster analysis Keyhole Markup Language (.kml) file which shall be used in task 2.Task 2 utilises the cluster outputs produced in task 1. Students then import the .kml cluster analysis layer produced by SaTScan into ArcGIS (or QGIS if preferred) and overlay this layer over a publicly available dataset that contains multiple spatial indexes. This socio-economic dataset uses real-world data on a region of the UK. This process allows students to visually explore the possible characteristics of a region that may explain where clusters fall. For example, students can choose to layer a measure of deprivation (The Index of Multiple Deprivation) over cluster output to visually examine the socio-economic characteristics of individual clusters. The assessment for this task is a student-led presentation and discussion based on their own critical thinking about which factors may predict cluster membership as well as maps that reflect these ideas. This activity is designed to help students develop their visual presentation and interpretation skills and become familiar with linking spatial factors to epidemiological trends.This activity is associated with a chapter in Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).

Texas Tech University Library’s AI Literacy workshop series developed weekly for spring 2024. Included here are materials related to part 2. This resource can be used as a general starting point for evaluating generative AI.Additionally, the workshop utilizes Padlet to facilitate discussion for active learning. Sessions can be held online, in-person, or hybrid. These sessions are also for broad appeal, and included faculty, staff, graduate students, and undergraduate students in attendance. The Padlets for this session included an evaluative Jeopardy-like game where participants could rate whether they felt a piece of media (text or image) was AI generated or "real," ie, human-made.Learning objectives for this session included:Utilize AI detection tools for their courses.Understand current Texas Tech policies related to AI use in the classroom and within research.Understand other ways of evaluating AI created materials. This resource drew on different aspects within the ACRL Framework, including Searching as Strategic Exploration.Included in the documentation is an outline with discussion questions. There are no slides for this workshop. 

Texas Tech University Library’s AI Literacy workshop series developed weekly for spring 2024. Included here are materials related to part 1. This resource can be used as a general starting point for introducing and understanding the technology. Additionally, the workshop utilizes Padlet to facilitate discussion for active learning in a hybrid setting. Sessions can be held online, in-person, or hybrid. These sessions are also for broad appeal, and included faculty, staff, graduate students, and undergraduate students in attendance.Learning objectives for this session included:Understand what generative AI is.Understand the background of generative AI in higher education.Understand the biases and other problems inherent in AI systems.This resource drew on many different aspects within the ACRL Framework.Included in the documentation is an outline with discussion questions and slides. 

This resource was designed as a jumping-off point for discussions between librarians and peer tutors who work outside of the library, specifically undergraduate subject-specific and writing tutors. Tutors are asked to examine ACRL framework by considering a learning objective and a brief related video. Videos were created by North Kentucky University's Steely Library. 

The Hidden Layer Workshop introduces key generative AI (genAI) concepts through a privacy lens. Participants probe the possibilities and limitations of genAI while considering implications for intellectual privacy, intellectual property, data sovereignty, and human agency. An original PROMPT Design Framework and worksheet guide participants through the iterative process of prompting generative AI to optimize output by specifying Persona, Requirements, Organization, Medium, Purpose, and Tone. In the centerpiece activity, participants engage in a hidden layer simulation to develop a conceptual understanding of the algorithms in the neural networks underlying LLMs and their implications for machine bias and AI hallucination. Drawing on Richards’s theory of intellectual privacy (2015) and the movement for data sovereignty, and introducing an original framework for the ethical evaluation of AI, Hidden Layer prepares participants to be critical users of genAI and synthetic media.The workshop is designed for a 60-minute session, but can be extended to fill the time available.Includes workshop guide, presentation slides, learning activities, and assessment instrument.

Vetting Sources:An exercise that teaches ChatGPT’s limitations. This exercise empowers students to verify the information AI generates, fostering responsible AI use.Ask ChatGPT to generate a list of 4 academic sources on a topic of your choice, and then evaluate the credibility and usability of those sources.Now answer:What is the topic you chose?What 4 citations were generated? (Paste the citations here)THEN complete the following:1. Are the citations actually real? Does such a journal/website/book exist? State which are not real and which are real. State whether any website used in a real citation where you found it is credible and why.2. State where those specific real citations are available full-text (check our library databses too). List the names of the places you found them (for example, name of such-and-such webite, name of database , etc...).3. Check the credentials of the lead author by doing a google search of their name in quotes. Are they trained in the field of the topic? State their credentials and/or academic degrees.4. Now run their name (in quotemarks) in a library database (like ProQuest or Ebscohost), use a drop down to search for AUTHOR - do they appear? IF YES, What are their other article/s (provide the permalink URLs) about?5. Now run a search for your same chosen topic in a library database. What are the top four most relevant (provide the four permalink URLs)? Note if they match any of the original four generated.Bonus 1 point: Talk about paid and unpaid access to this AI tool (look at pricing for different versions on the Chatgpt website) and how YOU think it might affect what you find in any tier of paid/unpaid access. This assignment tracks to the ACRL Information Literacy framework:"Information has Value"