Table 1 Summary of studies on AI application in higher education institutions in Latin America that met the inclusion criteria
From: Artificial intelligence applications in Latin American higher education: a systematic review
ID | Author(s), year | Country | AI tecnique | Tools | Algorithms used | AI application | Education topic | |
|---|---|---|---|---|---|---|---|---|
[1] | Bedregal-Alpaca et al. (2020) | Peru | ML (Prediction) | JAVA, SPSS Modeler, SPSS Statistics, EXCEL | MLP, DT (ID3 & C4.5) | Predictive modelling in education | Dropout and retention | |
[2] | Bojorque and Pesántez-Avilés (2020) | Ecuador | ML (Prediction) | n.d. | MLP-ANN | Predictive modelling in education | Teaching performance | |
[3] | Castrillón et al. (2020) | Colombia | ML (Prediction) | WEKA | J48 | Predictive modelling in education | Student performance | |
[4] | Chacón-Sánchez et al. (2020) | Colombia | ML (Classification) | WEKA | J48, J48Graft, NB, RT | Intelligent analytics | Student future development | |
[5] | Choque-Díaz et al. (2018) | Peru | NLP (Chatbot) | IBM Cloud platform, IBM Watson cognitive services | n.d. | Assistive technology (chatbots) | University services | |
[6] | Contreras et al. (2020) | Colombia | ML (Prediction) | Python | DT, SVM, MLP, KNN | Predictive modelling in education | Student performance | |
[7] | Cordero et al. (2020) | Ecuador | NLP (Chatbot) | Scrum, Extreme Programming (XP), IBM Watson™ Assistant | n.d. | Assistive technology (chatbots) | University services | |
[8] | da Fonseca Silveira et al. (2019) | Brazil | ML (Prediction) | Geocode, H2O AI, R | GLM, GBM, RF | Predictive modelling in education | Dropout and retention | |
[9] | Dehon et al. (2018) | Brazil | NLP (Chatbot) | LMS Moodle, Facebook Messenger chatbot, Facebook Notifier (Moodle plug-in) | n.d. | Assistive technology (chatbots) | Teacher-student communication | |
[10] | Delahoz-Dominguez et al. (2020) | Colombia | ML (Classification) | R | DT, RF | Intelligent analytics | University performance | |
[11] | Espinosa Rodríguez et al. (2018) | Mexico | NLP (Chatbot) | Facebook Messenger Chatbot, MongoDB mLAB | n.d. | Assistive technology (chatbots) | Student health and well-being | |
[12] | Fiallos et al. (2017) | Ecuador | NLP (Social Network Analysis) | n.d. | Force Atlas 2, TF-IDF model, LDA model | AI computer-assisted content analysis | University performance | |
[13] | García-González and Skrita (2019) | Colombia | ML (Prediction) | R | CT | Predictive modelling in education | Student performance | |
[14] | García-Vélez et al. (2019) | Ecuador | ML (Prediction) | scikit-learn toolkit | MLP | Predictive modelling in education | Student performance | |
[15] | Gómez Cravioto et al. (2020) | Mexico | ML (Prediction) | WEKA | J48, REPTree, RF | Predictive modelling in education | Student future development | |
[16] | Gutiérrez et al. (2018) | Mexico | ML & NLP (Sentiment Analysis) | R | SVM k-linear, k-radial, k-poly), RF | AI computer-assisted content analysis | Teaching performance | |
[17] | Klos et al. (2021) | Argentina | NLP (Chatbot) | Facebook Messenger Chatbot, SPSS | n.d. | Assistive technology (chatbots) | Student health and well-being | |
[18] | Mendoza Jurado (2020) | Bolivia | ML & NLP (Automatic review) | Python (Bag of Words), Tokenizer class from Keras (Python Deep Learning API) | NLP, MLP-ANN | AI computer-assisted content analysis | Assessment and evaluation | |
[19] | Menezes et al. (2020) | Brazil | DL (Facial recognition) | FaceNet architecture, Image capturing devices | HOG, CNN | Image analytics | Assessment and evaluation | |
[20] | Miranda and Guzmán (2017) | Chile | ML (Prediction) | SQL server, SPSS (MLP, DT), WEKA (BN) | MLP, DT, BN | Predictive modelling in education | Dropout and retention | |
[21] | Nieto et al. (2019) | Colombia | ML (Prediction) | KNIME | DT, RF, LR | Predictive modelling in education | University performance | |
[22] | Okoye et al. (2020) | Mexico | NLP (Sentiment Analysis) | R, Word Cloud | get_nrc_sentiment function get_sentiment function | AI computer-assisted content analysis | Teaching performance | |
[23] | Palacios et al. (2021) | Chile | ML (Prediction) | WEKA | DT, KNN, LR, NB, RF, SVM | Predictive modelling in education | Dropout and retention | |
[24] | Sandoval-Palis et al. (2020) | Ecuador | DL (Prediction) | R, SPSS, Orange | MLP-ANN | Predictive modelling in education | Student performance | |
[25] | Santos et al. (2020) | Brazil | ML (Prediction) | Python | EvolveDTree (GA & DT), KNN, AdaBoost, SVC, MLP, RF, QDA, NB | Predictive modelling in education | Dropout and retention | |
[26] | Sayama et al. (2019) | Brazil | DL & NLP (Reading comprehension) | Python (Natural Language Toolkit), Python (Deep Learning AI) | BiDAF, NLTK library, Tensorflow library | AI computer-assisted content analysis | University services | |
[27] | Tapia-Leon et al. (2017) | Ecuador | NLP (Knowledge Extraction) | PSPP (free version of SPSS), Python (Natural Language Toolkit) | NLTK library | AI computer-assisted content analysis | Teaching performance | |
[28] | Torres Soto et al. (2019) | Mexico | DL (Prediction) | Python (libraries Keras and Tensorflow) | ANN | Predictive modelling in education | Student health and well-being | |
[29] | Ulloa Cazarez and López Martín (2018) | Mexico | ML (Prediction) | n.d. | RBF, MLP, GR | Predictive modelling in education | Student performance | |
[30] | Villaseñor et al. (2017) | Mexico | ML (Classification) | LabSOM system, Scientometric tools (SJCR and SIR) | Self-organizing Map (SOM) family of neural networks | Intelligent analytics | University performance | |
[31] | Visbal-Cadavid et al. (2019) | Colombia | ML (Prediction) | R (Caret & nnet packages) | MLP-ANN | Predictive modelling in education | University performance | |