Even before being “baptized” and identified as an area of study, Artificial Intelligence was seen as a field with high transformative power. Nowadays, we already see the results of this in all sectors in which it is used, including Digital Marketing.
The adoption of artificial intelligence in educational institutions and educational marketing is no longer a differentiator, but is now considered essential for competitiveness.
According to a recent survey, approximately 88% of marketing professionals already use AI tools in their routines, automating processes, personalizing campaigns, and increasing the effectiveness of their strategies. The global AI market in education is projected to grow from $5.88 billion in 2024 to $32.27 billion by 2030, with an annual growth rate of 31.2%
Although there is a certain fear, due to lack of knowledge, that we will be replaced by machines, the truth is that interaction with them produces much more efficient results. No wonder, between 2018 and 2019, companies that implemented resources with artificial intelligence grew between 4% and 14%, according to a survey by Gartner.
Within this universe full of possibilities, deep learning emerges as a sub-area with great potential for investment, especially when it comes to increasing funding rates and student retention.
Deep learning is available presently including our chatbots, a technology that is still only used by 42.2% of the 500 largest educational institutions in the country. The adoption of extremely responsive virtual assistants capable of adopting natural language has a positive impact on the experience of students and future customers, speeding up service.
Continue reading this article to discover how its use can make a big difference in Educational Marketing.
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Contextualization of machine learning
Before we explain better what deep learning is and how it works, we need to talk about the machine learning, that is, machine learning. In short, this is one of the several branches of artificial intelligence where algorithms collect massive data. And it is precisely from this information that the equipment learns.
With minimal human interference, the machine can modify its own behavior and make predictions. As you go through new experiences, you are able to adjust and offer responses that are more relevant to the context.
The logic is always the same, but some contours vary and give rise to different types of learning. Three, more precisely.
In supervised learning, the machine has a kind of tutor and the process takes place based on labeled data. Basically, the equipment is taught, through classification and regression techniques, into which class or category it should organize the information it receives.
In unsupervised learning, the machine does not receive this initial guidance. The data is not labeled and you have no control over what will be obtained. Through grouping, association and dimension reduction, a strategy is used to identify patterns that are not yet very clear in the data.
And last but not least, reinforcement learning, which differs a bit from previous models. In this case, the process occurs through trial and error. For every hit the machine makes, it is rewarded. For every mistake, she is punished. This makes you adjust your responses and patterns to maximize the results obtained.
As you can see, there is no single way to teach machines, although they all involve the use of algorithms. Delving deeper into machine learning, we ended up arriving at deep learning, which we will talk about in the next topic, specifically about how it can be advantageous for Educational Marketing.
Advantages for Educational Marketing
The 4RevOps works with artificial intelligence and has consistently used deep learning as a tool to promote retention and recruitment of students of partner educational institutions. One Educational Marketing increasingly technological, focused and results-driven is our main objective.
An example of this is the use of systems that qualify leads according to the objective. The software “learns” from the database of students already enrolled at the institution, discovering the most common actions and profile.
Mass personalization, enabled by AI and deep learning solutions. Advanced tools can now create individualized learning paths, hyper-targeted ads, and dynamic experiences for each potential student, based on their behavior and specific needs.
Furthermore, the advent of generative AI — such as natural language chatbots and assistants that support teachers in content creation — makes relationships with the teaching public more assertive and engaging.
Then, and with little or no human intervention, it indicates which contacts have the greatest potential for enrollment. This information is valuable as it saves time and resources in preparing a fundraising strategy.
The same system can make predictions about students who are at risk of dropping out, allowing targeted campaigns to build loyalty and maintenance.
In times of crisis, like the one we are experiencing now, it is essential to invest in what brings a guaranteed return. The use of deep learning tools allows you to discover precisely where, what and how to bet, as there is no time or resources for trial and error.
SEE TOO:
- Artificial Intelligence and its help in student retention;
- Artificial Intelligence in education: future becomes reality;
- How to register using artificial intelligence?
Understanding the deep learning
Deep learning It is also known as deep learning or constant learning. Using this technology allows machines to learn on their own through pattern recognition. So far, nothing new in relation to what we discussed previously. The difference lies in the type of algorithm used in this process.
A base for deep learning lies in the use of artificial neural networks, a specific category of algorithms that was developed to “imitate” biological neural networks. The degree of complexity is even greater because these networks work in layers, each of them with a different interpretation of the data to which the machine was exposed.
This type of neural networks began to be developed in the 1950s. Much more complex today, they are now used in different types of services and products. Two of the most popular applications are voice recognition and facial recognition, which demonstrate the great computational power of this technology.
In practice, to understand deep learning It is necessary to understand how these artificial neural networks work. When the machine receives information, it goes to the input layer and then goes on to processing. Here, the numerous hidden layers contribute different data and then the obtained result goes to the output layer.
While in a simple neural network there is only one processing layer, a deep neural network has several. This translates into a faster and, probably, more accurate response.
Caption: Illustration of artificial neural networks representing how deep learning works in educational marketing.
Deep learning no Marketing
There are those who say that deep learning is artificial intelligence in its essence, being truly transformative. And it's still true. Since it began to be widely used, technology has changed dynamics, streamlined processes, reduced costs and even introduced new possibilities in different industries.
In Marketing, more specifically in Digital Marketing, this scenario was no different. Much of this success comes from the fruitful relationship between DL and big data. This expression, increasingly used, refers to the large volume of structured and unstructured data that is generated.
The employment of deep learning in the processing and analysis of big data has proven to be efficient, as long as supervised and unsupervised learning techniques are used together. This makes it possible to reveal more precisely the behavior patterns and build strategies focused on these trends.
In Brazil, deep learning-based solutions are already present, including in public sectors. One example is the launch of the institutional chatbot Mara by the Federal University of Maranhão (UFMA) in 2024, which uses artificial intelligence to automate administrative processes and optimize service for both students and staff.
Other innovative tools, such as AI-powered automatic correction systems for handwritten essays, are already being adopted in dozens of cities, demonstrating how deep learning is revolutionizing learning support and school management in Brazil.
Machines are capable of acting quickly, recognizing the most subtle changes and even allowing some predictions to be made based on these pattern changes.
For strategic use of deep learning, it is essential to rely on an experienced company that has a multidisciplinary team to guide the work and interpretation of data. Mkt4edu, despite its young age, already has a lot of experience in the subject, something visible in the more than 3,000 campaigns created in these years of activity.
Here, tools such as Leadscore and Watson Machine Learning are important in measuring results. These models are generated through experiment, with the weights of each field present in the training data. Next, updated data is formulated, creating a new list.
No matter how advanced deep learning solutions become, ethical regulation and the human role remain fundamental. The growing use of AI demands attention to student data privacy and algorithm transparency.
Educators need to be prepared both technically and ethically to act as advisors, curators, and interpreters of automated responses, fostering a symbiosis between human creativity and computational efficiency. The educational marketing of the future will be hybrid, leveraging technology to serve pedagogical purposes and enhance relationships with students and families.
These additions reflect the most current practices and trends, supported by relevant articles published in 2025, and reinforce Mkt4edu's positioning as a reference in the sector. I didn't detect any critical outdated information in the original text—the old data remains accurate, but can now be enriched with new information that adds value and authority to the content.
If you need to insert hyperlinks directly, I recommend linking the highlighted keywords ("adopt AI tools," "mass personalization," "institutional chatbot Mara," "ethical regulation and the human role") to the corresponding articles cited in the paragraphs, ensuring alignment with SEO best practices.
Want to know better how this works? Schedule a meeting with us to know how deep learning can make your institution prosper!
How is deep learning revolutionizing educational marketing?
What is deep learning and how does it differ from machine learning?
Deep learning is a subfield of artificial intelligence that uses multilayered neural networks to interpret and learn from data. The main difference from machine learning lies in its depth and analytical complexity, which enables more accurate and real-time responses.
How does deep learning support student acquisition and retention?
It enables detailed behavioral analysis of leads and students, identifying patterns to predict who is more likely to enroll or drop out. This allows institutions to implement more effective conversion and retention strategies while saving resources.
What are the advantages of AI-powered chatbots in education?
AI chatbots with deep learning provide personalized support, using natural language and accurate responses. They streamline tasks like invoice issuance, document submission, and academic assistance, enhancing student satisfaction and experience.
What are artificial neural networks and why are they important?
They are computational structures inspired by the human brain, made of layers that hierarchically process data. In deep learning, these networks analyze data deeply, improving marketing predictions and automating decision-making processes.
What types of machine learning are explained in the article?
The article highlights three types: supervised learning (labeled data), unsupervised learning (no labels and pattern detection), and reinforcement learning (trial and error). These underpin applications such as deep learning in marketing.
Why is deep learning essential for educational marketing strategies?
Because it enables large-scale personalization, precise segmentation, and campaign automation with high relevance. This strengthens relationships with prospects and increases both conversion and retention rates.
What are some examples of deep learning applications in Brazil?
The article cites UFMA’s chatbot Mara, which automates academic services. It also mentions AI systems for handwritten essay correction, already used in many cities, showing how deep learning is transforming educational support in the country.
Does deep learning replace the human role in educational institutions?
No. Despite the automation and intelligence of these tools, the human role remains essential for interpreting data, curating content, and ensuring ethical use. The future of educational marketing is hybrid, combining technology and human creativity.
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