Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2023 series only includes data for quarter 2 and quarter 3. The GIS coordinates for schools in the Eastern Cape are incorrectly entered in the original data from the DBE. The data entered in the GIS_long variable is incorrectly entered into the GIS_lat variable. This issue only occurs for schools in the Eastern Cape (EC), all other GIS coordinates for all the other provinces is correct. Therefore, for geospatial analysis, users can swap the GIS coordiate data only for the Eastern Cape.

The tabular and visual dataset focuses on South African basic education and provides insights into the distribution of schools and basic population statistics across the country. This tabular and visual data are stratified across different quintiles for each provincial and district boundary. The quintile system is used by the South African government to classify schools based on their level of socio-economic disadvantage, with quintile 1 being the most disadvantaged and quintile 5 being the least disadvantaged. The data was joined by extracting information from the debarment of basic education with StatsSA population census data. Thereafter, all tabular data and geo located data were transformed to maps using GIS software and the Python integrated development environment. The dataset includes information on the number of schools and students in each quintile, as well as the population density in each area. The data is displayed through a combination of charts, maps and tables, allowing for easy analysis and interpretation of the information.

Abstract

The data collected from the SNAP Survey of Ordinary Schools is collected from all schools each year. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education and the Provincial education departments, as well as to provide valuable information to external stakeholders. For example, general school data from the survey is used to compile and maintain the Master List of Schools in the country for education planning purposes.

Geographic coverage

The survey has national coverage

Analysis unit

The units of analysis in the survey are schools in South Africa and their staff and learners

Universe

The survey covers all Ordinary Schools in South Africa, both Public and Independent. This survey does not cover Special Schools as the DBE conducts a separate survey of Special Schools annually.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

Data is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record the EMIS number provided by the DBE on the questionnaires and forms for identification.

Data appraisal

The data files do not cover the same period. Dates covered for each file are:

Applicable school grades data file (2010-2013) General school data file (2007-2013) Learner enrolment data files (1997-2013) Master list data file (1997-2013) Remuneration of practitioners data file (2010-2013) Staff remuneration data file (1997-2013)

The 1997 and 1998 data in this dataset cannot be matched with other years as the learner count is too low. The 1997 data also does not include data on schools in the Eastern Cape Province and the Limpopo Province The 1998 data does not include data on schools in the Limpopo Province

The Applicable grades data file does not include unique identifiers.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2021 series only includes data for quarter 1 and quarter 2.

South Africa ZA: School Enrollment: Primary: % Net data was reported at 80.030 % in 2015. This records a decrease from the previous number of 85.878 % for 2005. South Africa ZA: School Enrollment: Primary: % Net data is updated yearly, averaging 88.013 % from Dec 1970 (Median) to 2015, with 13 observations. The data reached an all-time high of 92.585 % in 1995 and a record low of 64.903 % in 1970. South Africa ZA: School Enrollment: Primary: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2020 series includes data for quarter 1, quarter 2, quarter 3 and quarter 4.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2022 series only includes data for quarter 2 and quarter 3.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2018 series only includes data for quarter 3 and quarter 4.

South Africa ZA: School Enrollment: Secondary: Private: % of Total Secondary data was reported at 4.187 % in 2014. This records an increase from the previous number of 4.107 % for 2012. South Africa ZA: School Enrollment: Secondary: Private: % of Total Secondary data is updated yearly, averaging 2.936 % from Dec 1999 (Median) to 2014, with 15 observations. The data reached an all-time high of 4.187 % in 2014 and a record low of 2.295 % in 2000. South Africa ZA: School Enrollment: Secondary: Private: % of Total Secondary data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This dataset is about universities in South Africa. It has 13 rows. It features 3 columns: total students, and undergraduate students.

South Africa ZA: Children Out of School: Female: % of Female Primary School Age data was reported at 14.608 % in 2015. This records an increase from the previous number of 8.116 % for 2005. South Africa ZA: Children Out of School: Female: % of Female Primary School Age data is updated yearly, averaging 6.806 % from Dec 1970 (Median) to 2015, with 13 observations. The data reached an all-time high of 34.732 % in 1970 and a record low of 4.570 % in 1999. South Africa ZA: Children Out of School: Female: % of Female Primary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Education Statistics. Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2019 series only includes data for quarter 3 and quarter 4.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2017 series only includes data for quarter 1.

Dilapidated public school infrastructure can be found across the country, but the problem is particularly acute in the Eastern Cape where the majority of the so called 'mud schools' are located. On 04 February 2011, following court action on the issue of mud schools, the Legal Resources Centre, acting on behalf of 7 schools and the Centre for Child Law, concluded a landmark settlement with the National Department of Basic Education in which the Department committed to spend R8.2 billion from 1 April 2011 to 1 March 2014 to eradicate mud schools and improve infrastructure of schools throughout South Africa. The Centre for Child Law commissioned Cornerstone Economic Research, to track school infrastructure spending and delivery. The aim of the research was to assess what progress has been made in addressing the issues that brought about the litigation. This study, amongst other things, makes the concerning finding that the Department has woefully underspent the allocated school infrastructure funding for two years running. The target for the number of schools to be built in 2011/2012 and 2012/2013 was 49. However, only 10 schools had been completed at the end of the first year.

This dataset is about universities in South Africa. It has 13 rows. It features 2 columns including foundation year.

Abstract

The Master List of Schools is a record of all schools in South Africa. The data forms part of the national Education Management Information Systems (EMIS) database used to inform education policymakers and managers in the Department of Basic Education (DBE) and the Provincial education departments, as well as to provide valuable information to external stakeholders. The list is maintained by provincial departments and regularly sent to DBE for updating. A key function of the master list is to uniquely identify each school in the country through a school identifier called the EMIS number. Additionally, the list contains data on school quintiles - categories (quintiles) based on the socioeconomic status of the community in which the school is situated. Analyses comparing schools' performance often use school quintiles as control measures for socioeconomic status, to take into account the effect of, for example, poor infrastructure, shortage of materials and deprived home backgrounds on school performance. There are also other basic data fields in the school master list that could provide the means to answer some of the most frequently asked questions about learner enrolment, teachers and learner-teacher ratio of schools. It is a useful dataset for education planners and researchers and is even widely used in the private sector by those who regularly deal with schools.

Geographic coverage

The data has national coverage

Analysis unit

Individuals and institutions

Universe

The survey covers all schools (ordinary and special needs) in South Africa, both public and independent.

Kind of data

Administrative records and survey data

Mode of data collection

Other

Research instrument

Data from the SNAP survey and ANA that are used to compile the Master List of Schools is collected with a survey questionnaire and educator forms. The principle completes the survey questionnaire and each educator (both state paid and other) in each school completes an educator form. Schools record their EMIS number provided by the DBE on the questionnaire and form for identification.

Data appraisal

The 2016 series only includes data for quarter 1, quarter 2 and quarter 3.

This dataset is about universities in South Africa. It has 13 rows. It features 2 columns including logo link.

Abstract

The National Income Dynamics Study (NIDS) is a face-to-face longitudinal survey of individuals living in South Africa as well as their households. The survey was designed to give effect to the dimensions of the well-being of South Africans, to be tracked over time. At the broadest level, these were:

· Wealth creation in terms of income and expenditure dynamics and asset endowments · Demographic dynamics as these relate to household composition and migration · Social heritage, including education and employment dynamics, the impact of life events (including positive and negative shocks), social capital and intergenerational developments · Access to cash transfers and social services

This administrative dataset is for schools attended by NIDS respondents. The dataset was created by matching the names of schools with Department of Education (DoE) registered lists of schools in South Africa. A detailed description of the matching process is provided in the user manual, which includes a description of the inherent limitations associated with conducting such an exercise. As such, the comparison of Wave 1 and Wave 2 information provides a detailed picture of how South Africans have fared over two years of very difficult socio-economic circumstances.

Geographic coverage

National

Analysis unit

Households

Universe

The target population for NIDS was private households in all nine provinces of South Africa, and residents in workers' hostels, convents and monasteries. The frame excludes other collective living quarters, such as student hostels, old age homes, hospitals, prisons and military barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A stratified, two-stage cluster sample design was employed in sampling the Dwelling units to be included in the base wave. In the first stage, a sample of 400 Primary Sampling Units (psus)2 was drawn (by statisticians at Stats SA) from Stats SA's 2003 Master Sample of 3000 psus. At the time that the 2003 Master Sample was compiled, eight non-overlapping samples of ten or twelve dwelling Units were systematically drawn within each PSU. Each of these samples is termed A “cluster” by Stats SA. These clusters were then allocated to the various household Surveys that were conducted by Stats SA between 2004 and 2007 (such as the Labour Force Surveys, General Household Surveys and the 2005/06 Income and Expenditure Survey). However, two clusters in each PSU were never used by Stats SA and these were allocated to NIDS.

In the first stage, a sample of 400 PSUs had to be drawn from the 3000 PSUs in the Master Sample. The explicit strata in the Master Sample are the 53 district councils (DCs). The sample was proportionally allocated to these 53 strata and PSUs were selected within strata with probability proportional to size. It should be noted that the sample was not designed to be representative at provincial level, implying that analysis of the results at the province level is not recommended.

Mode of data collection

Face-to-face [f2f]

Response rate

Over the combined field work periods NIDS fieldworkers knocked on 10,642 household doors. Of these households, 7305 agreed to participate and the interview was completed. This equates to a 69% response rate. The total sample for NIDS consists of 409 PSUs. Of those, 9 were replaced in phase 2 because the whole PSU was inaccessible in phase 1. They are therefore excluded from the rest of the calculations.

The overall aim of the USAID/SA basic education program is to improve primary grade reading outcomes by building teacher effectiveness and strengthening classroom and school management. This is being accomplished through support to innovative, local interventions that have a demonstrated capacity for scale-up. The main USAID/SA program is the School Capacity and Innovation Program (SCIP), which also leverages significant private sector resources, amplifying the impact of USAID’s investment in the South African education system. SCIP is co-funded by The ELMA Foundation and J.P. Morgan and designed in collaboration with the South African Department of Basic Education. SCIP supports local South African models or interventions that work directly with teachers and school management teams in innovative ways in order to improve their practice as instructional leaders and managers. SCIP is aligned to the USAID Global Education Strategy (2011–2015) which supports interventions to improve learning outcomes with a focus on primary grade reading as a measure of performance. In addition to seeking initiatives that demonstrate innovation and impact, sustainability and scalability are key components of the SCIP program. The Strengthening Teaching of Early Language and Literacy (STELLAR) Program improves the language and literacy skills of Grade R children from disadvantaged communities in South Africa by training and supporting Grade R teachers. Grade R (also called the Reception Year) is the year of schooling before Grade 1.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.