This document discusses the integration of artificial intelligence (AI) in higher education, focusing on its applications like machine learning and natural language processing to enhance teaching and learning. It presents a proposed framework for predicting student placement success using machine learning algorithms and outlines the methodology for data collection, exploration, and model training. The findings indicate that AI has the potential to improve educational outcomes, although it also raises concerns about data privacy and security.

1. ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION:
A LITERATURE STUDY AND
PROPOSED FRAMEWORK
Presenting at the
International conference on
"Recent Trends in Pure and
Applied Sciences" Organized by
Smt. Kusumtai Rajarambapu Patil
Kanya Mahavidyalaya, Islampur,
Dist. Sangli, Maharashtra, India.
Authors:
• R.M. Huddar
• P.P. Chavan
• V. B. Gaikwad
• R.S. Kamath
• R. K. Kamat

2. ABSTRACT
• Although the use of technology in education has always been significant,
the increased availability of smart devices and web-based curriculum has
expanded its current prevalence. The use of AI in teaching and learning is
extremely important.
• This study of the literature intends to examine the use of artificial
intelligence, natural language processing, and machine learning in
education.

3. INTRODUCTION
 The field of study known as artificial intelligence focuses on the
development of intelligent computer systems that can perform
tasks in a manner analogous to those performed by humans (AI).
 The application of AI approaches aims to make it possible for
computers to acquire knowledge from data, modify their behaviour
in response to novel inputs, and carry out activities that require
thinking, sensing, planning, and decision-making.
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4. Presentation
title
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Artificial
intelligence consists of a
collection of subfields that
are coordinated in order to
accomplish a certain mission
or objective. In particular
ML, Robotics
and NLP.

5. 1] Artificial Intelligence:
Artificial Intelligence technology is
used in various fields:
 recommendation engine for
shopping
 facial recognition for smart devices
 Robotics
 In health care
for detecting diseases
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6. 6
2] Machine learning:
Involves the construction of models
and the discovery of patterns within
datasets through the application of
data, statistics, and algorithms. It
uses supervised learning,
unsupervised learning, and
reinforcement learning approaches.

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ML Algorithms :
1. Unsupervised Machine learning:
The main goal of unsupervised learning is to discover hidden and interesting patterns
in unlabeled data.
2. Supervised Machine learning
Machine learning method in which models are trained using labeled data.
3. Reinforcement learning
method based on rewarding desired behaviors and/or punishing undesired ones. e.g. games

8. NLP is a field of Artificial Intelligence that
gives the machines the ability to read,
understand and derive meaning from
human languages.
Spam detection: spam emails
Machine translation: Google Translate
Text summarization: to create
summaries and synopses

9. Literature study:
• A literature study is a summary
of the earlier written works on a
subject.
• In the study, we have looked at a
number of articles, journals, and
research papers on the topic of
artificial intelligence in education.
Presentation title 9

10. PROPOSED FRAMEWORK:
In the current study we have
proposed a model for prediction
of placement success of the
students using machine
learning algorithm.
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11. 11
Techniques employed for the model

12. Python Libraries: NumPy, Pandas,
Matplotlib, Sklearn
The data we're going to use has been labelled, so
we're going to employ Supervised Machine
learning for the placement prediction.
Methodology: The proposed framework comprises
of various phases namely Data collection, Data
Exploration and pre-processing, Training and
testing of the model.
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13. 1. Data collection: From the institute database, the
students' placement information from the previous
year will be gathered. The sample size would be 300
students, combing MBA and MCA students.
Variables in this case:
Ø 10th percentage
Ø 12th percentage
Ø Graduation percentage,
Ø Post-graduation sem1, sem2 and sem3 percentages
Ø Post-graduation specialization
Ø Placed company type
Ø Communication skills
Ø Programming skills
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14. 2. Data exploration and Pre-processing :
This phase involves various steps:
• Importing the dataset and generating a data frame from a csv file using the
Pandas library.
• Cleaning up the data, identifying missing values, and preparing it for future
analysis are all part of data exploration.
• To find missing values in Pandas DataFrame, utilise the isnull() and notnull()
functions. These operations establish if a value is NaN or not.
• By utilising the replace() function and substituting mean for a null value
• Utilising the matplotlib library in Python to visualise the data.

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3. Training and Validation of the model:
Training the dataset using Logistic regression algorithm. We'll split the data
into two parts. We'll use 70% of the dataset for training and 30% for other
testing.
• Splitting data using train_test_split() method by importing sklearn package
• test_size= 0.3 i.e., 30% for testing the data and 70% for training
• train_test_split() performs the split and returns four sections:
x_train: The training part of the first section (x)
y_train: The training part of the second section (y)
x_test: The test part of the first section (x)
y_test: The test part of the second section (y)

16. 4. Building the model:
Building the model using logistic regression model on the training
set​
- Importing the class logistic regression and assigning a variable to it.​
- The variable is fitted with the data to be trained​
- Predict() function is used to predict for the test set
- Comparing y_test and the predicted value
The model is estimated using the function fit() and prepared to make
predictions based ​on the test data.
This is accomplished by using the function predict() and the
independent testing ​variables (x_test).
To check if the model is sound, the results can be compared to actual
values (y_test).​
5. Evaluating accuracy:
• Evaluating the accuracy of the results.
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17. • The logistic or
sigmoid function has
an S-shaped curve
or sigmoid curve
with the y-axis
ranging from 0 and
1.
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Fig. Logistic regression sigmoid function graph

18. FINDINGS
Logistic regression can be an effective tool for predicting whether
students will be placed or not, with accuracy rates ranging from
75% to 85%. However, it's important to note that the accuracy of
the model may depend on various factors, such as the quality and
quantity of data used to train the model, the selection of features,
and the generalizability of the model to different contexts.
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19. CONCLUSION:
• Artificial intelligence technologies have positive and
negative effects on education. AI can automate the
teaching-learning process resulting in convenient
education. Although it has some disadvantages in
particular data security and privacy.
• AI is transforming higher education is by providing a
more hands-on approach to learning. For instance, AI
can simulate complex scenarios, enabling students to
apply their theoretical knowledge and gain experience
before entering the workplace. This approach can
significantly reduce the skill gap, which is essential in
filling the industry's demand for proficiency.
Presentation title 19

20. REFERENCES
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• Mduma, N., Kalegele, K., & Machuve (2019). A survey of Machine Learning Approaches and Techniques
for Student Dropout Prediction. Data Science Journal.
• Nagar, R., Singh, Y. (2019). A literature survey on Machine Learning Algorithms. Journal of Emerging
Technologies and Innovative Research, 6(4). https://www.jetir.org/papers/JETIR1904C77.pdf
• Zawacki-Richter, O., Marin, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on
artificial intelligence applications in higher education – where are the educators? International Journal of
Educational Technology in Higher Education, 16, 1-27. doi: 10.1186/s41239-019-0171-0.
Presentation title 20

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23. Thank You!
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