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Discover the fascinating world of generative AI in education through my captivating blog! In this immersive guide, we’ll explore:

The Magic of Visual Storytelling: Discover how AI can convert ordinary text into remarkable visuals, enriching the learning experience for students.

Mastering Python for Creative AI: Get hands-on with Python to implement powerful text-to-image models like Dreambooth-Stable-Diffusion.

Dive Deep into Cutting-edge Algorithms: Understand the inner workings of state-of-the-art models and their applications in educational settings.

Empower Personalization in Education: Explore how AI can personalize content for each learner, delivering tailored and captivating visual stories.

Prepare for the Future of Learning: Stay ahead of the curve by embracing AI-driven technologies and their potential to revolutionize education.

This article was published as a part of the Data Science Blogathon.

Table of Contents Project Description

In this project, we will delve into a deep learning method to produce quality images from textual descriptions, specifically targeting applications within the education sector. This approach offers significant opportunities for enriching learning experiences by providing personalized and captivating visual stories. By leveraging pre-trained models such as Stable Diffusion and GPT-2, we will generate visually appealing images that accurately capture the essence of the provided text inputs, ultimately enhancing educational materials and catering to a variety of learning styles.

Problem Statement

The primary objective of this project is to create a deep learning pipeline capable of generating visually engaging and precise images based on textual inputs. The project’s success will be gauged by the quality and accuracy of the images generated in comparison to the given text prompts, showcasing the potential for enriching educational experiences through captivating visuals.


To successfully follow along with this project, you will need the following:

A good understanding of deep learning techniques and concepts

Proficiency in Python programming.

Familiarity with libraries such as OpenCV, Matplotlib, and Transformers.

Basic knowledge of using APIs, specifically the Hugging Face API.

This comprehensive guide provides a detailed end-to-end solution, including code and output harnessing the power of two robust models, Stable Diffusion and GPT-2, to generate visually engaging images from the textual stimulus.

Stable Diffusion is a generative model rooted in the denoising score-matching framework, designed to create visually cohesive and intricate images by emulating a stochastic diffusion process. The model functions by progressively introducing noise to an image and subsequently reversing the process, reconstructing the image from a noisy version to its original form. A deep neural network, known as the denoising score network, guides this reconstruction by learning to predict the gradient of the data distribution’s log-density. The final outcome is the generation of visually engaging images that closely align with the desired output, guided by the input textual prompts.

GPT-2, the Generative Pre-trained Transformer 2, is a sophisticated language model created by OpenAI. It builds on the Transformer architecture and has undergone extensive pre-training on a substantial volume of textual data, empowering it to produce a contextually relevant and coherent text. In our project, GPT-2 is employed to convert the given textual inputs into a format suitable for the Stable Diffusion model, guiding the image generation process. The model’s ability to comprehend and generate contextually fitting text ensures that the resulting images align closely with the input prompts.

Combining these two models’ strengths, we generate visually impressive images that accurately represent the given textual prompts. The fusion of Stable Diffusion’s image generation capabilities and GPT-2’s language understanding allows us to create a powerful and efficient end-to-end solution for generating high-quality images from text.


Step 1: Set up the environment

We begin by installing the required libraries and importing the necessary components for our project. We will use the Diffusers and Transformers libraries for deep learning, OpenCV and Matplotlib for image display and manipulation, and Google Drive for file storage and access.

# Install required libraries !pip install --upgrade diffusers transformers -q # Import necessary libraries from pathlib import Path import tqdm import torch import pandas as pd import numpy as np from diffusers import StableDiffusionPipeline from transformers import pipeline, set_seed import matplotlib.pyplot as plt import cv2 from google.colab import drive

Step 2: Access the dataset

We will mount Google Drive to access our dataset and other files in this step. We will load the CSV file containing the textual prompts and image IDs and update the file paths accordingly.

# Mount Google Drive drive.mount('/content/drive') # Update file paths data = pd.read_csv('/content/drive/MyDrive/SD/promptsRandom.csv', encoding='ISO-8859-1') prompts = data['prompt'].tolist() ids = data['imgId'].tolist() dir0 = '/content/drive/MyDrive/SD/'

Using OpenCV and Matplotlib, we will display the images from the dataset and print their corresponding textual prompts. This step allows us to familiarize ourselves with the data and ensure it has been loaded correctly.

# Display images for i in range(len(data)): img = cv2.imread(dir0 + 'sample/' + ids[i] + '.png') # Include 'sample/' in the path plt.figure(figsize=(2, 2)) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.axis('off') print(prompts[i]) print()

Step 4: Configure the deep learning models: We will define a configuration class (CFG) to set up the deep learning models used in the project. This class specifies parameters such as the device used (GPU or CPU), the number of inference steps, and the model IDs for the Stable Diffusion and GPT-2 models.

We will also load the pre-trained models using the Hugging Face API and configure them with the necessary parameters.

# Configuration class CFG: device = "cuda" seed = 42 generator = torch.Generator(device).manual_seed(seed) image_gen_steps = 35 image_gen_model_id = "stabilityai/stable-diffusion-2" image_gen_size = (400, 400) image_gen_guidance_scale = 9 prompt_gen_model_id = "gpt2" prompt_dataset_size = 6 prompt_max_length = 12 # Replace with your Hugging Face API token secret_hf_token = "XXXXXXXXXXXX" # Load the pre-trained models image_gen_model = StableDiffusionPipeline.from_pretrained( CFG.image_gen_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=secret_hf_token, guidance_scale=9 ) image_gen_model = prompt_gen_model = pipeline( model=CFG.prompt_gen_model_id, device=CFG.device, truncation=True, max_length=CFG.prompt_max_length, num_return_sequences=CFG.prompt_dataset_size, seed=CFG.seed, use_auth_token=secret_hf_token )

Step 5: Generate images from prompts: We will create a function called ‘generate_image’ to generate images from textual prompts using the Stable Diffusion model. The function will input the textual prompt and model and generate the corresponding image.

Afterward, we will display the generated images alongside their corresponding textual prompts using Matplotlib.

# Generate images function def generate_image(prompt, model): image = model( prompt, num_inference_steps=CFG.image_gen_steps, generator=CFG.generator, guidance_scale=CFG.image_gen_guidance_scale ).images[0] image = image.resize(CFG.image_gen_size) return image # Generate and display images for given prompts for prompt in prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(prompt) print()

Our project also experimented with generating images using custom textual prompts. We used the ‘generate_image’ function with a user-defined prompt to showcase this. In this example, we chose the custom prompt: “The International Space Station orbits gracefully above Earth, its solar panels shimmering”. The code snippet for this is shown below:

custom_prompt = "The International Space Station orbits gracefully above Earth, its solar panels shimmering" generated_image = generate_image(custom_prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(custom_prompt) print()

Let’s create a simple story with five textual prompts, generate images for each, and display them sequentially.


A lonely astronaut floats in space, surrounded by stars.

The astronaut discovers a mysterious, abandoned spaceship.

The astronaut enters the spaceship and finds an alien map.

The astronaut explores the new planet, filled with excitement and wonder.

Now, let’s write the code to generate and display images for each prompt:

story_prompts = [ "A lonely astronaut floats in space, surrounded by stars.", "The astronaut discovers a mysterious, abandoned spaceship.", "The astronaut enters the spaceship and finds an alien map.", "The astronaut decides to explore the new planet, filled with excitement and wonder." ] # Generate and display images for each prompt in the story for prompt in story_prompts: generated_image = generate_image(prompt, image_gen_model) plt.figure(figsize=(4, 4)) plt.imshow(generated_image) plt.axis('off') print(prompt) print()#import csv

Executing the above code will generate images for each story prompt, displaying them sequentially along with their corresponding textual prompts. This demonstrates the model’s ability to create a visual narrative based on a sequence of textual prompts, showcasing its potential for storytelling and animation.


This comprehensive guide explores a deep learning approach to generate visually captivating images from textual prompts. By harnessing the power of pre-trained Stable Diffusion and GPT-2 models, an end-to-end solution is provided in Python, complete with code and outputs. This project demonstrates the vast potential deep learning holds in industries that require custom and unique visuals for various applications like storytelling, which is highly useful for AI in Education.

5 Key Takeaways: Harnessing Generative AI for Visual Storytelling in Education

Importance of Visual Storytelling in Education: The article highlights the significance of visual storytelling in enhancing the learning experience by engaging students, promoting creativity, and improving communication skills.

Python Implementation: The article provides a step-by-step Python guide to help educators and developers harness the power of generative AI models for text-to-image synthesis, making the technology accessible and easy to integrate into educational content.

Potential Applications: The article discusses various applications of generative AI in education, such as creating customized learning materials, generating visual aids for storytelling, and assisting students with special needs, like visual impairments or learning disabilities.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


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Top 6 Use Cases Of Generative Ai In Education

Due to the COVID-19 pandemic, the use of digital technologies to enhance education has significantly increased as many students around the world have had to shift to online learning. For example, investment in education for adopting innovative technologies increased from $7 billion to $20 billion during the pandemic as trends suggest. However, digital technologies also have the potential to transform the education experience in other ways beyond just online classes. The application of generative AI in education is an example to this.

Generative AI is a digital technology that can quickly create new and realistic visual, textual, and animated content. In other articles, we investigated its use cases in different  sectors, such as healthcare and banking. While other technologies like conversational AI and robotic process automation (RPA) are implemented in education, generative AI is not properly implemented in education. Despite this, it has potential use cases for improving it. This article explains the top 6 potential ways to use generative AI in education.

1. Personalized Lessons

Personalized lesson plans are a powerful way to ensure that students receive the most effective education tailored specifically to their needs and interests. These lesson plans can be generated by using AI-powered algorithms to analyze student data, such as:

Their past performance

Their skills 

And any feedback they might have given regarding content

AI-based systems can leverage such information to generate customized curriculum that is more likely to engage each student and help them reach their potential. This can be important for children with learning disabilities or disorders.

For example, Speechify is a generative AI-driven tool. It offers text-to-speech or speech-to-text generations on desktops or on online use.

2. Course Design

Generative AI tools can help design and organize course materials, including syllabi, lesson plans, and assessments. They can also personalize course material based on students’ knowledge gaps, skills and learning styles, such as practice problems or interactive exercises. 

Generative AI can create simulations and virtual environments once paired with other technologies, such as virtual reality. Consequently, it offers more engagement and interactive courses, improving students’ learning experience.   

For example, a generative AI system could create a virtual laboratory setting where students can conduct experiments, observe the results, and make predictions based on their observations.

3. Content Creation for Courses 

Generative AI can assist in creating new teaching materials, such as questions for quizzes and exercises or explanations and summaries of concepts. This can be especially useful for teachers who need to create a large amount and a variety of content for their classes. By using AI, it is possible to create modified or brand-new content from the original content.

Furthermore, generative AI can facilitate generating additional materials to supplement the main course materials, such as: 

Reading lists

Study guides 

Discussion questions 



Also, AI can generate scripts for video lectures or podcasts, streamlining multimedia content creation for online courses. Image generation is another important ability of generative AI for education. Teachers may want to generate images with specific modifications that respond to particular course needs.

For example, NOLEJ offers an e-learning capsule that is AI generated in only 3 minutes. This capsule provides an interactive video, glossary, practice, and summary for a target topic (see Figure 1 below).

More established companies are using AI to generate content that supports their main products. For instance, Duolingo, a language learning platform, uses GPT-3 to correct French grammar and create items for their English test. The company concludes that with the implementation of GPT-3, second language writing skills of customers are increased.

4. Data Privacy Protection for Analytical Models

Using synthetic data, which is created by AI models that have learned from real-world data, can provide anonymity and protect students’ personal information. Synthetic data sets produced by generative models are effective and useful for training other algorithms, while being secure and safe to use.

For more on how generated synthetic data enables data privacy, you can check out these articles:

5. Restoring Old Learning Materials

Generative AI can improve the quality of outdated or low-quality learning materials, such as historical documents, photographs, and films. By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media.

These updates can also make it easier for students to read, analyze, and understand the materials, leading to a deeper understanding of the content and, ultimately, better learning outcomes.

Using a version of generative AI, Generative Adversarial Networks (GANs), it is possible to restore low-quality images and remove simple watermarks. In Figure 2 below, you can see a prototype for image restoration via GANs. Such image restoration can be adapted to educational materials. For example, in art and design schools, restoring old images would provide the detection of important details of artworks. Also in history classes and research, scanning and restoring old documents can be facilitated.

Figure 2. Image restoration with GANs. (Source: Towards Data Science)

6. Tutoring 

Another use case of generative AI is to provide tutoring. Generative AI can be used to create virtual tutoring environments, where students can interact with a virtual tutor and receive real-time feedback and support. This can be especially helpful for students who may not have access to in-person tutoring.

According to academic studies, private tutoring children with severe reading difficulty improved their reading skills by 50% in a year. However, providing tutoring to all students can be a challenge. Generative AI can tackle this issue by creating virtual tutoring environments. In these environments, students can interact with a virtual tutor and receive feedback and support in real-time. This can be especially helpful for students who may not have access to in-person tutoring.

For example, TutorAI is trying to implement this kind of use of generative AI in education. It offers an educational platform that generates interactive content on a variety of topics.

Another generative AI work for teaching purposes can be the implementation of chatbots for tutoring. Chatbot Life’s 2023 chatbot report shows that education is the third biggest industry benefiting from chatbots.

Lately, Chat GPT from OpenAI stormed the internet with its ability to engage in highly personalized conversations and definitive answers. It can answer course-related questions from a variety of domains, and can even write essays on the target topic. 

On the other hand, implementing generative AI-based chatbots specified and regulated for educational purposes is a future plan. However, it offers potential uses and benefits:

One potential use would be to provide around-the-clock support to students and their parents, including help with homework.

Generative chatbots can also assist with administrative tasks, such as answering student or parent questions, freeing up time for educators to focus on other tasks, such as grading and lesson planning.

The flexibility and natural feeling of generative chatbots make them useful in educational settings, particularly with elementary and middle school children.

Challenges of generative AI in education

Although generative AI has a lot of potential to improve educational practices, it may also pose some potential challenges. These can be shortly listed as:

Biases in educational materials

False or inaccurate information

Abuse of it for self interest

Unemployment risks for some teachers or other education professionals

For a detailed discussion on the ethical challenges of generative AI, you can check our article.

For more on generative AI

To explore more about generative AI, you can check our other articles:

Discover the top generative AI tools from our detailed list sorted by category:

If you have questions regarding generative AI, feel free to reach out:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





What Is Generative Artificial Intelligence (Ai)?

Generative AI describes algorithms that can be utilized to create new content

Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content such as audio, code, images, text, simulations, and videos. Recent breakthroughs in the industry could radically change the way we approach content creation. The way we approach content creation could be drastically altered by recent breakthroughs in the field.

Machine learning encompasses generative AI systems, and one such system, ChatGPT, describes its capabilities as follows:

What are DALL-E and ChatGPT?

The generative pre-trained transformer (GPT) is receiving a lot of attention right now. It is a cost-free chatbot that can respond to almost any question. It was developed by OpenAI and will be made available to the public for testing in November 2023 and already regarded as the best AI chatbot ever.

Medical imaging analysis and high-resolution weather forecasts are just two examples of the many applications of machine learning that have emerged in recent years. It is abundantly clear that generative AI tools like ChatGPT and DALL-E can alter how a variety of tasks are carried out.

What Distinguishes Artificial Intelligence from Machine Learning?

Artificial intelligence is a type of machine learning. Models that can “learn” from data patterns without human guidance are developed through machine learning to develop artificial intelligence. The unmanageably colossal volume and intricacy of information (unmanageable by people, in any case) that is presently being produced has expanded the capability of AI, as well as the requirement for it.

How is a Generative AI Model Constructed?

Boldface-name donors have given OpenAI, the company behind ChatGPT, former GPT models, and DALL-E. Meta has released its Make-A-Video product, which is based on generative AI, and DeepMind is a subsidiary of Alphabet, the parent company of Google.

But it’s not just talent. Asking a model to practice using almost anything on the internet will cost you. OpenAI has not disclosed the exact cost but estimates that GPT-3 was trained on about 45 terabytes of text data-about a million square feet of bookshelf space, or a quarter of the entire Library of Congress-valued at several million dollars. These are not resources that your gardening business can use.

What Kinds of Outputs Can Be Generated by a Generative AI Model?

You may have noticed that the outputs produced by generative AI models can appear uncanny or indistinguishable from content created by humans. The match between the model and the use case, or input, and the quality of the model as we have seen, ChatGPT’s outputs appear to be superior to those of its predecessors so far determine the outcomes.

On-demand, AI-generated art models like DALL-E can produce strange and beautiful images like a Raphael painting of a child and a Madonna, eating pizza. Other generative artificial intelligence models can deliver code, video, sound, or business reproductions.

However, not all of the outputs are appropriate or accurate. Generative AI outputs are combinations of the data used to train the algorithms that have been carefully calibrated. Since how much information is used to prepare these calculations is so unquestionably huge-as noted, GPT-3 was prepared on 45 terabytes of text information-the models can seem, by all accounts, to be “inventive” while creating yields.

What Kinds of Issues Can Be Resolved by A Generative AI Model?

In a matter of seconds, generative AI tools can produce a wide range of credible writing and respond to criticism to make the writing more useful. This has suggestions for a wide assortment of ventures, from IT and programming associations that can profit from the momentary, generally right code produced by computer-based intelligence models to associations needing promoting duplicate.

The Generative Ai Revolution: A Glimpse Into The Future Of Work

The explosion of interest in artificial intelligence has drawn attention not only to the astonishing capacity of algorithms to mimic humans but to the reality that these algorithms could displace many humans in their jobs. The economic and societal consequences could be nothing short of dramatic.

The route to this economic transformation is through the workplace. A widely circulated Goldman Sachs study anticipates that about two-thirds of current occupations over the next decade could be affected, and a quarter to a half of the work people do now could be taken over by an algorithm. Up to 300 million jobs worldwide could be affected. The consulting firm McKinsey released its own study predicting an AI-powered boost of US$4.4 trillion to the global economy every year.

The implications of such gigantic numbers are sobering, but how reliable are these predictions?

I lead a research program called Digital Planet that studies the impact of digital technologies on lives and livelihoods around the world and how this impact changes over time. A look at how previous waves of such digital technologies as personal computers and the internet affected workers offers some insight into AI’s potential impact in the years to come. But if the history of the future of work is any guide, we should be prepared for some surprises.

The IT revolution and the productivity paradox

A key metric for tracking the consequences of technology on the economy is growth in worker productivity – defined as how much output of work an employee can generate per hour. This seemingly dry statistic matters to every working individual because it ties directly to how much a worker can expect to earn for every hour of work. Said another way, higher productivity is expected to lead to higher wages.

The Goldman Sachs study predicts productivity will grow by 1.5 percent per year because of the adoption of generative AI alone, which would be nearly double the rate from 2010 and 2023. McKinsey is even more aggressive, saying this technology and other forms of automation will usher in the “next productivity frontier,” pushing it as high as 3.3 percent a year by 2040.

That sort of productivity boost, which would approach rates of previous years, would be welcomed by both economists and, in theory, workers as well.

For a while, it seemed that the optimists would be vindicated. In the second half of the 1990s, around the time the World Wide Web emerged, productivity growth in the U.S. doubled, from 1.5 percent per year in the first half of that decade to 3 percent in the second. Again, there were disagreements about what was really going on, further muddying the waters as to whether the paradox had been resolved. Some argued that, indeed, the investments in digital technologies were finally paying off, while an alternative view was that managerial and technological innovations in a few key industries were the main drivers.

Regardless of the explanation, just as mysteriously as it began, that late 1990s surge was short-lived. So despite massive corporate investment in computers and the internet – changes that transformed the workplace – how much the economy and workers’ wages benefited from technology remained uncertain.

Early 2000s: New slump, new hype, new hopes

While the start of the 21st century coincided with the bursting of the so-called dot-com bubble, the year 2007 was marked by the arrival of another technology revolution: the Apple iPhone, which consumers bought by the millions and which companies deployed in countless ways. Yet labor productivity growth started stalling again in the mid-2000s, ticking up briefly in 2009 during the Great Recession, only to return to a slump from 2010 to 2023.

But before we could get there and gauge how these new technologies would ripple through the workplace, a new surprise hit: the COVID-19 pandemic.

The pandemic productivity push – then bust

Devastating as the pandemic was, worker productivity surged after it began in 2023; output per hour worked globally hit 4.9 percent, the highest recorded since data has been available.

Much of this steep rise was facilitated by technology: larger knowledge-intensive companies – inherently the more productive ones – switched to remote work, maintaining continuity through digital technologies such as videoconferencing and communications technologies such as Slack, and saving on commuting time and focusing on well-being.

While it was clear digital technologies helped boost productivity of knowledge workers, there was an accelerated shift to greater automation in many other sectors, as workers had to remain home for their own safety and comply with lockdowns. Companies in industries ranging from meat processing to operations in restaurants, retail, and hospitality invested in automation, such as robots and automated order-processing and customer service, which helped boost their productivity.

But then there was yet another turn in the journey along the technology landscape.

The 2023-2023 surge in investments in the tech sector collapsed, as did the hype about autonomous vehicles and pizza-making robots. Other frothy promises, such as the metaverse’s revolutionizing remote work or training, also seemed to fade into the background.

In parallel, with little warning, “generative AI” burst onto the scene, with an even more direct potential to enhance productivity while affecting jobs – at massive scale. The hype cycle around new technology restarted.

Looking ahead: Social factors on technology’s arc

Given the number of plot twists thus far, what might we expect from here on out? Here are four issues for consideration.

First, the future of work is about more than just raw numbers of workers, the technical tools they use, or the work they do; one should consider how AI affects factors such as workplace diversity and social inequities, which in turn have a profound impact on economic opportunity and workplace culture.

For example, while the broad shift toward remote work could help promote diversity with more flexible hiring, I see the increasing use of AI as likely to have the opposite effect. Black and Hispanic workers are overrepresented in the 30 occupations with the highest exposure to automation and underrepresented in the 30 occupations with the lowest exposure. While AI might help workers get more done in less time, and this increased productivity could increase wages of those employed, it could lead to a severe loss of wages for those whose jobs are displaced. A 2023 paper found that wage inequality tended to increase the most in countries in which companies already relied a lot on robots and that were quick to adopt the latest robotic technologies.

Second, as the post-COVID-19 workplace seeks a balance between in-person and remote working, the effects on productivity – and opinions on the subject – will remain uncertain and fluid. A 2023 study showed improved efficiencies for remote work as companies and employees grew more comfortable with work-from-home arrangements, but according to a separate 2023 study, managers and employees disagree about the impact: The former believe that remote working reduces productivity, while employees believe the opposite.

Third, society’s reaction to the spread of generative AI could greatly affect its course and ultimate impact. Analyses suggest that generative AI can boost worker productivity on specific jobs – for example, one 2023 study found the staggered introduction of a generative AI-based conversational assistant increased productivity of customer service personnel by 14 percent. Yet there are already growing calls to consider generative AI’s most severe risks and to take them seriously. On top of that, recognition of the astronomical computing and environmental costs of generative AI could limit its development and use.

Finally, given how wrong economists and other experts have been in the past, it is safe to say that many of today’s predictions about AI technology’s impact on work and worker productivity will prove to be wrong as well. Numbers such as 300 million jobs affected or $4.4 trillion annual boosts to the global economy are eye-catching, yet I think people tend to give them greater credibility than warranted.

Also, “jobs affected” does not mean jobs lost; it could mean jobs augmented or even a transition to new jobs. It is best to use the analyses, such as Goldman’s or McKinsey’s, to spark our imaginations about the plausible scenarios about the future of work and of workers. It’s better, in my view, to then proactively brainstorm the many factors that could affect which one actually comes to pass, look for early warning signs and prepare accordingly.

The history of the future of work has been full of surprises; don’t be shocked if tomorrow’s technologies are equally confounding.

This article is republished from The Conversation under a Creative Commons license. Read the original article written by Bhaskar Chakravorti, Dean of Global Business, The Fletcher School, Tufts University.

Ai In Marketing: Comprehensive Guide

Thanks to the increasing amount of data gathered from customers, businesses’ marketing strategies become more data-driven. Considering AI is better than humans when it comes to processing and analyzing large datasets, it is not a surprise that AI is becoming widespread among marketers. According to a study, 87% of companies that have adopted AI were using it to improve email marketing. 61% of marketers were also planning to use artificial intelligence in sales forecasting.

Why is AI in marketing important?

Source: Google Trends

Level of interest in using marketing in AI is increasing. Marketers are always chasing ways to increase their impact without increasing marketing budgets. AI technologies provide them a way to achieve these. Marketers can combine customer data and AI-powered tools to anticipate their customers’ next move and improve their journey. To do that, they can use AI to

understand the market better,

create unique content,

and perform personalized marketing campaigns.

provide accurate insights and suggest smart marketing solutions that would directly reflect on profits.

Below is our initial framework explaining the impact of AI in marketing. We have developed that over time, and you can see our latest view below the framework. To get more information, please visit the relevant page to see references, videos, and detailed explanations:

1-Optimize Pricing & Placement

Dynamic pricing: Businesses can optimize their pricing strategy by using AI to scrape competitors’ pricing data automatically and to maximize revenue by setting competitive prices for products.

Physical placement

Merchandising optimization: You can leverage machine learning and big data to optimize your online or offline merchandising. 

Shelf audit/analytics: Your business can use videos, images, or robots in the retail area to audit and analyze your use of shelf space. You can identify and manage stock-outs or sub-optimal use of shelf space. One of the most leading-edge examples in this area is Lowe’s autonomous retail service robot. It will explore how robots can meet the needs of both customers and employees. Meet the LoweBot

More mainstream solutions in this space include leveraging images taken by employees to manage and analyze shelf space. Trax Image Recognition explains their solution in this space in detail.

Digital Placement

Product Information Management: Businesses manage and improve all product information centrally to improve product discoverability and appeal. As a result, they automatically modify and update the product description, box description, and all other related information.

Visual Search Capability: Businesses can leverage machine vision to enable their customers to search for products by image or video to immediately reach their desired products. In the today’s world too easy to ensure customer’s desires with the AI algorithms thanks to the lots of snapping and sharing images. For example, Flipkart, one of India’s largest online retailers, uses visual search.

Image tagging to improve product discovery: You can leverage machine vision to tag your images, taking into account your users’ preferences and relevant context for your products.

2-Optimize marketing communication

An optimized marketing communication reaches customers at the right time at the right channel with the right message. There are numerous emerging AI companies specializing in these areas. For example, companies like Appzen track customers’ cross-device behavior to ensure that your messages target customers at the device they are using at the time of your marketing communication.

Just a few years ago, most messages to customers were handcrafted for specific macro-segments. Today companies like Phrasee suggest personalized messages to ensure that customers receive messages they prefer to read. For example, Dell has increased its page visits by 22% by introducing Persado’s AI-powered marketing tools.


Neuromarketing: Your business can leverage neuroscience and biometric sensors to understand how your content impacts your audience’s emotions and memory. You can test your content in private until it achieves the desired effect.

Omnichannel: Businesses can personalize marketing communication across different paid marketing platforms. They can create a coherent strategy for the overall marketing strategy and analyze comparatively the impact on different platforms.

Retargeting: You can retarget customers who have already expressed interest in your products or services. As a result, sales numbers increase by engaging the right customer.

Channel specific optimizations

Content generation: For content creators, it is always a challenge to come up with a matchless marketing strategy. In this field, AI can provide creative solutions. From the topics chosen for content marketing, Natural Language Generation models can create unique content and deliver creative suggestions. You can read more about content generation by reading our article.

Mobile Marketing: With mobile marketing, businesses can create personalized, individual messaging based on each customer’s real-time and historical behaviors. Most of the users are active in mobile platforms, develop your strategy to gain a greater share in mobile traffic.

Email Marketing: Email marketing includes emails that are tailored to individual behavior. Detect which email type performs better with your product and customer. Customize by including the necessary email structure and images.

Video Commerce: You can make videos with embedded products shoppable by inserting relevant links and auto-identify products in videos.

3- Personalized Marketing Strategies

Businesses can leverage customer data to reach customers with personalized recommendations via email, site search, or other channels. These systems ensure your company to make the right offer taking into account all

digital and analog interactions (page views, purchases, email opens…) of that customer with your brand

that customer’s interactions on different web properties

interactions of similar customers

These are all designed to lead the consumer more reliably towards a sale and support companies to increase their revenues. 

If you are looking for vendors in the space, feel free to check our transparent, data driven vendors lists on the topic:

4- Connect & Leverage Customer Feedback

Emotion Recognition: You can capture the emotional state of your customers by analyzing micro gestures and mimics. Computer vision will help you to capture the details and provide you with the real emotions of your customer.

5- Analytics

Analytics: You connect all your marketing data and KPIs automatically. AI tools enable you to manage campaigns, trigger alerts, and improve your marketing efficiency. AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before.  

Channel specific analytics

Social analytics & automation: Businesses can leverage Natural Language Processing and machine vision to analyze and act upon all content generated by your actual or potential customers on social media, surveys, and reviews.

PR analytics: You can learn from, analyze, and measure your PR efforts by using AI tools for marketing. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. With the increasing demand in digital media to specify the targets will more easy for the brands in competitive marketing.

If you believe you can benefit from AI in your business, you can view our data-driven lists of AI Consultants, and AI/ML Development Services

Now that you have checked out AI applications in marketing, feel free to look at our AI in the marketing section. You can check out AI applications in sales, customer service, IT, data, or analytics. And if you have a business problem, we can help you find the right solution to resolve it:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.





Education Research Highlights From 2023

A look at the research that made an impact in 2023, from the benefits of well-designed classroom spaces to the neuroscience behind exercise and math ability.

2023 was a great year for education research. fMRI technology gave us new insight into how exercise can improve math ability by changing the structure of children’s brains (#13 below). We saw how Sesame Street’s 40-year history has made an impact on preparing young children for school (#7). Several studies reinforced the importance of social and emotional learning for students (#2, 5, and 9). Two must-read publications were released to help educators understand how students learn (#4 and 11). Here are 15 studies published this year that every educator should know about.

1. Well-Designed Classrooms Boost Student Learning

A classroom’s physical learning space makes a difference in how well students learn. In this study of 27 schools in England, researchers found that improving a primary classroom’s physical design, including lighting, layout, and decorations, can improve academic performance by as much as 16 percent (although too many decorations can be a distraction).

Barrett, P. S., Zhang, Y., Davies, F., & Barrett, L. C. (2023). Clever Classrooms: Summary report of the HEAD project. University of Salford, Manchester.

2. The Benefits of Being Kind Last From Kindergarten to Adulthood

Kindness matters. Kindergarten students who share, help others, and show empathy are more likely to have personal, educational, and career success as adults, finds this study that tracked 753 children from 1991 to 2010.

Jones, D. E., Greenberg, M., Crowley, M. (2023). Early social-emotional functioning and public health: The relationship between kindergarten social competence and future wellness. American Journal of Public Health, e-View Ahead of Print.

3. Theatre Programs Help Students With Autism

Did you know that participating in theatre programs can help students with autism learn to play in groups, communicate with others, and recognize faces? These are the findings of a study by researchers from Vanderbilt University.

Corbett, B. A., Key, A. P., Qualls, L., Fecteau, S., Newsom, C., Coke, C., & Yoder, P. (2023). Improvement in Social Competence Using a Randomized Trial of a Theatre Intervention for Children With Autism Spectrum Disorder. Journal of Autism and Developmental Disorders, 1-15.

4. The Science of Learning

If you’re looking for an excellent review of research on how students learn, check out The Science of Learning. Drawing from cognitive science, this report breaks down the research into six principles with a full reference list and teaching tips.

Deans for Impact (2023). The Science of Learning. Austin, TX: Deans for Impact.

5. Investing $1 in Social and Emotional Learning Yields $11 in Long-Term Benefits

We know that SEL has tremendous benefits for student learning, but what are the long-term economic benefits? Researchers analyzed the economic impact of six widely-used SEL programs and found that on average, every dollar invested yields $11 in long-term benefits, ranging from reduced juvenile crime, higher lifetime earnings, and better mental and physical health.

Belfield, C., Bowden, B., Klapp, A., Levin, H., Shand, R., & Zander, S. (2023). The Economic Value of Social and Emotional Learning. New York, NY: Center for Benefit-Cost Studies in Education.

6. Low-Income Students Now a Majority

51 percent of the students across the nation’s public schools now come from low-income families.

A New Majority Research Bulletin: Low Income Students Now a Majority in the Nation’s Public Schools

7. Sesame Street Boosts Learning for Preschool Children

Sesame Street was introduced over 40 years ago an educational program to help prepare children for school. Examining census data, researchers discovered that preschool-aged children in areas with better reception did better in school. Children living in poorer neighborhoods experienced the largest gains in school performance.

Kearney, M. S., & Levine, P. B. (2023). Early Childhood Education by MOOC: Lessons From Sesame Street (No. w21229). National Bureau of Economic Research.

8. Don’t Assign More Than 70 Minutes of Homework

For middle school students, assigning up to 70 minutes of daily math and science homework was beneficial, but assigning more than 90-100 minutes resulted in a decline in academic performance. Read more about the research on homework.

Fernández-Alonso, R., Suárez-Álvarez, J., & Muñiz, J. (2023). Adolescents’ Homework Performance in Mathematics and Science: Personal Factors and Teaching Practices. Journal of Educational Psychology, 107(4), 1075–1085

9. Mindfulness Exercises Boost Math Scores

Mindfulness exercises help students feel more positive, and a new study found that it can also boost math performance. Elementary school students that participated in a mindfulness program had 15 percent better math scores, in addition to several emotional and psychological benefits.

10. Boys Get Higher Math Scores When Graded by Teachers Who Know Their Names

In this Israeli study, middle and high school students were randomly assigned to be graded anonymously or by teachers who knew their names. Despite performing worse than girls in math when graded anonymously, boys had better scores when teachers knew who they were.

Lavy, V., & Sand, E. (2023). On the Origins of Gender Human Capital Gaps: Short and Long Term Consequences of Teachers’ Stereotypical Biases (No. w20909). National Bureau of Economic Research.

11. Top Psychology Principles Every Teacher Should Know

How do students think and learn? The American Psychological Association sought to answer this question with the help of experts across a wide variety of psychological fields. The result: 20 science-backed principles that explain how social and behavioral factors influence learning.

American Psychological Association, Coalition for Psychology in Schools and Education. (2023). Top 20 Principles from Psychology for PreK–12 Teaching and Learning.

12. To Help Students With ADHD Concentrate, Let Them Fidget

Since hyperactivity can be a natural state for students with ADHD, preventing them from fidgeting can hurt their ability to stay focused. For tips on how to let students fidget quietly, check out 17 Ways to Help Students With ADHD Concentrate.

Hartanto, T. A., Krafft, C. E., Iosif, A. M., & Schweitzer, J. B. (2023). A trial-by-trial analysis reveals more intense physical activity is associated with better cognitive control performance in attention-deficit/hyperactivity disorder. Child Neuropsychology, (ahead of print), 1-9.

13. The Neuroscience Behind Exercise and Math Ability

Research shows that exercise has a positive effect on learning, but studies generally tend to be observational. With the use of fMRI technology, however, researchers have gained new insight into how people learn. A team of scientists examined the brain structures of children and found that when young children exercise, their brains produce a thinner layer of cortical gray matter, which can lead to stronger math skills.

Chaddock-Heyman, L., Erickson, K. I., Kienzler, C., King, M., Pontifex, M. B., Raine, L. B., Hillman, C. H., & Kramer, A. F. (2023). The Role of Aerobic Fitness in Cortical Thickness and Mathematics Achievement in Preadolescent Children. PLOS ONE, 10(8), e0134115.

14. The Benefits of a Positive Message Home

Getting parents more involved in their child’s education is a great way to boost student learning. When teachers sent short weekly messages to parents with tips on how their kids could improve, it led to higher-quality home discussions and cut course dropout rates by almost half.

Kraft, M. A., & Rogers, T. (2023). The underutilized potential of teacher-to-parent communication: Evidence from a field experiment. Economics of Education Review, 47, 49-63.

15. When Teachers Collaborate, Math and Reading Scores Go Up

Teaching can feel like an isolating profession, but this new study shows that working in groups — especially instructional teams — can boost student learning.

Ronfeldt, M., Farmer, S. O., McQueen, K., & Grissom, J. A. (2023). Teacher Collaboration in Instructional Teams and Student Achievement. American Educational Research Journal, 52(3), 475-514.

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