• Understand generative AI's impact on Marketing
• Learn effective prompting strategies
• Develop practical applications for teaching
• Identify research opportunities
• Create discipline-specific AI integration plans
Intro
Current AI Landscape
• Latest developments in generative AI
• Key players and platforms
• Industry adoption trends
• Educational implications
• Academic implications
• Ethical considerations
Intro
Faculty AI Usage Assessment
• Current adoption levels
• Common applications
• Implementation challenges
• Success stories
• Areas for improvement
2024 - 2025
Workshop Impact Metrics
Expected faculty adoption rate: 85%
Curriculum integration potential: 70% of courses
Student exposure to AI tools: 100% increase
The workshop is designed to accelerate AI adoption in academic settings, focusing on practical applications in Marketing disciplines.
Based on current academic AI integration trends - Gartner Education Insights, 2024
Gen AI in Marketing
GenAI
Market Research & Opportunity Identification
AI analyzes competitors' strategies, product launches, and consumer engagement to identify market gaps and opportunities through data-driven insights.
Accelerates product development by approximately 5% time to market. [Source]
GenAI
Sentiment Analysis
Uses AI and NLP to analyze customer feedback from reviews and surveys, categorizing it into themes and sentiments to understand customer opinions.
T-Mobile achieved 73% reduction in customer complaints through proactive issue resolution. [Source]
GenAI
Enhanced Customer Service
AI chatbots handle tasks like processing returns, providing product information, and resolving complex customer service issues.
Manages up to 80% of routine inquiries, reducing costs by up to 30%. [Source]
GenAI
Content Ideation & Creation
LLMs analyze existing content to generate new articles, social media posts, and personalized email content that reflect brand tone and style.
68% of companies observed growth in content marketing ROI after integrating AI. [Source]
GenAI
Behavioral Targeting
Real-time data analysis allows for tailored content and recommendations, increasing relevance to individual users.
Meta uses LLMs to analyze user behavior in real-time, optimizing ad targeting for unconventional audiences. [Source]
GenAI
Image & Video Production
Brands can swiftly generate images, video advertisements, and product demonstrations, streamlining the creative process.
AI-generated visuals can boost consumer engagement, compared to generic product photos.
GenAI
Audio & Voice-overs
AI voice-over tools transform written materials into engaging podcasts and support development of multilingual audio content.
Voice content shows 24% higher recall rate for advertisements. [Source]
GenAI
Search Engine Optimization
AI analyzes user behavior and search patterns to categorize keywords based on intent, enabling creation of content aligned with user expectations.
AI-driven SEO campaigns resulted in higher conversion rates for e-commerce businesses. [Source]
Image-to-Text and Text-to-Image Models
Image
Brand Asset Generation
Text-to-image models create custom marketing visuals, product mockups, and social media graphics from text descriptions.
Reduces design time and costs while maintaining brand consistency. [Source]
Image
Visual Content Personalization
Creates customized visual content for different audience segments, improving engagement through relevance.
Personalized AI-generated visuals associated with boost in consumer engagement. [Source]
Image
Product Photography Enhancement
AI tools can enhance product photos, remove backgrounds, adjust lighting, and create consistent product imagery.
Maintains visual consistency across product lines while reducing photography costs.
Image
Visual Data Extraction
Image-to-text models extract information from competitor marketing materials, consumer reviews, and social media posts.
Enables comprehensive competitive analysis and consumer sentiment tracking.
Speech-to-Text (STT) and Text-to-Speech (TTS)
Audio
Podcast & Audio Content Creation
TTS technology transforms written marketing content into engaging audio formats with natural-sounding voices.
Voice content shows 24% higher recall rate for advertisements. [Source]
Audio
Multilingual Voice-overs
Creates localized audio content in multiple languages without hiring voice actors for each language.
Blueair's Amazon Ads campaign using AI audio resulted in 94% higher add-to-cart rate. [Source]
Audio
Customer Feedback Analysis
STT converts customer calls, video testimonials, and focus groups into searchable text for sentiment analysis.
Enables comprehensive analysis of customer feedback across multiple channels.
Audio
Audio Advertising
Creates personalized audio ads for streaming platforms, podcasts, and smart speakers with custom voices.
Delivers targeted messaging with consistent brand voice across audio channels.
Audio
Passive Audio Data Mining
Collects and analyzes ambient audio from consumer devices to extract behavioral patterns, conversation topics, and emotional responses for creating detailed marketing profiles.
Enables hyper-targeted advertising through unprecedented insights into consumer habits and preferences captured in private environments.
Recent Applications
Retail Marketing
Case
2024
Michaels' AI-Powered Personalization
Increased personalized email campaigns from 20% to 95%
Boosted email click-through rates by 25%
Significantly improved customer engagement and conversion rates
Michaels implemented an AI platform that analyzes customer data to create highly personalized marketing campaigns, dramatically increasing the reach and effectiveness of their email marketing efforts.
L'Oréal analyzes millions of online comments for product opportunities
Marketers are using generative AI to analyze competitor moves, assess consumer sentiment, and test new product opportunities. Rapid generation of response-ready product concepts improves the efficiency of successful products, increases testing accuracy, and accelerates time to market.
45.3% of detail page views from new-to-brand customers
94% higher add-to-cart rate compared to 2024 average
Interactive audio ads delivered through Amazon Alexa
Blueair leveraged Amazon Ads' generative AI solution, Audio Generator, to produce interactive audio advertisements on Alexa, significantly outperforming traditional marketing channels in both customer acquisition and conversion metrics.
Seamless integration with clinic management software for healthcare providers
Enables synchronized marketing data for enhanced outreach efforts
Intuit's Mailchimp integrated AI capabilities through Intuit Assist to optimize marketing strategies, particularly benefiting healthcare providers by enabling seamless synchronization with clinic management systems like Clinicminds for aesthetic clinics and MedSpas.
Used GPT-4 and DALL·E for creative content generation
Allowed consumers to generate Coke-themed artwork
Deployed AI chatbots in interactive advertisements
Coca-Cola leveraged advanced AI models including GPT-4 and DALL·E for their holiday marketing campaign, creating interactive experiences that allowed consumers to generate personalized Coke-themed artwork and engage with AI chatbots.
Analyzed 485 million data points across Southeast Asian markets
Identified top 50 trending recipes in each country
Highlighted innovative uses of cereal in various dishes
Kellogg's utilized AI to analyze massive datasets from social media, recipe sites, and forums across Malaysia, the Philippines, Singapore, and Thailand, uncovering regional food trends and innovative cereal applications to inform their marketing strategy.
Analyzes millions of online comments, images, and videos
Monitors over 3,500 sources for emerging trends
Informs product innovation and marketing strategies
L'Oréal utilizes artificial intelligence to analyze vast amounts of online content from thousands of sources, enabling early detection of emerging beauty trends that inform both product development and targeted marketing campaigns.
Creates personalized content across email, mobile, and web channels
Drafts personalized responses to improve customer support efficiency
Salesforce's Einstein GPT leverages OpenAI's models to enhance customer relationship management through AI-generated content, improving engagement and streamlining customer support processes.
Experiential Narratives: Comparing AI and Human Content
Reviews by human experts showed higher levels of embodied cognition than AI-generated ones
ChatGPT 3.5 demonstrated more positive affect in product reviews
More advanced ChatGPT 4 created content with higher lexical diversity than human copywriters
AI-generated unbranded content drove higher purchase intention in controlled experiments
Researchers examined generative AI's capability to create experiential narratives in marketing by comparing AI-generated content with human-written reviews and social media posts. While automated text analysis revealed differences in key metrics like embodied cognition and lexical diversity, human raters often failed to detect these differences. The study highlights both opportunities for effective AI application in marketing content and risks like hallucination in AI-generated product descriptions.
LLM-generated qualitative data rated as more detailed and insightful than human-only data
Human-LLM hybrids outperformed both human-only and LLM-only methods in generating insights
Incorporating few-shot learning and RAG improved response heterogeneity and reliability in quantitative research
LLMs demonstrated effectiveness in both data generation and analysis across research methodologies
Researchers investigated how human-LLM hybrid approaches can enhance marketing research through a comprehensive framework for integrating AI across the research process. Their study with a Fortune 500 food company replicated both qualitative (Friendsgiving) and quantitative (refrigerated pet food) studies using GPT-4. The findings demonstrate that AI-human hybrids deliver significant efficiency and effectiveness gains compared to traditional methods, with LLMs proving particularly valuable for streamlining discussion guides, identifying research participants, generating synthetic respondents, and analyzing unstructured data.
Technology-augmented marketing era replacing value-based marketing
Five critical themes: AI, robots, digital marketing, big data, and sustainability
Market demand shows 25% earnings premium for AI-skilled candidates
Study provides practical AI exercises for marketing classrooms
Researchers examined how technological advancements have transformed marketing practice and education. They propose marketing educators must reconsider pedagogical approaches across class conduct, topics, assignments, and technology integration. The study provides practical examples for integrating AI tools into exercises like marketing research, customer behavior analysis, and sales negotiations.
GenAI projected to enhance marketing productivity by up to 15% of total marketing expenditure
Marketing identified as the most affected firm function by GenAI adoption
Key research areas include co-ideation, market research, persuasion, and customer engagement
Analysis of how GenAI impacts both firm-level marketing capabilities and consumer-level creativity
Researchers developed a comprehensive roadmap examining how GenAI transforms marketing innovation processes. Their framework analyzes four key phases: developing (co-creation with consumers), testing (market research), communicating (persuasion and disclosure), and engaging (customer interaction beyond transactions). The study highlights both the opportunities for leveraging GenAI's creative capabilities and concerns about its impact on consumers' creative skills and marketing's contribution to firm value.
Aspect-Based Sentiment Analysis of Patient Feedback
Analyzed approximately 15,000 entries from patient.info medical forum
Identified seven distinct aspect types in patient feedback
LLMs with few-shot learning outperformed other deep learning models
ChatGPT 3.5 achieved 90% accuracy in aspect and sentiment detection
Researchers developed a comprehensive aspect-based sentiment analysis framework for patient feedback using content analysis, deep learning, and large language models. The study extracted key aspects including medicines, medical procedures, diagnoses, and medical staff interactions, analyzing sentiment across these categories. This approach provides healthcare providers with granular insights into patient experiences beyond traditional satisfaction metrics, offering actionable data for improving healthcare delivery and treatment effectiveness.
Aspect-Based Review Analysis Using ChatGPT for Hotel Service Failures
Successfully employed ChatGPT to analyze 8,539 hotel reviews from TripAdvisor
Identified ten key hotel attributes for aspect-based analysis
Demonstrated ChatGPT's effectiveness in summarizing customer feedback and explicit keyword extraction
Revealed different complaint patterns across budget, midrange, and luxury hotel segments
Researchers employed ChatGPT to conduct aspect-based analysis of hotel complaint reviews, categorizing feedback into predefined attributes like room quality, service quality, and cleanliness. The methodology demonstrated how effectively ChatGPT can identify service failures across different hotel market segments, providing deeper insights into customer experiences than traditional analysis methods. This approach represents a significant advancement in automated text analysis for hospitality management.
Agreement rates between human and LLM-generated perceptual data reached over 75%
LLMs demonstrated effectiveness for both brand similarity measures and product attribute ratings
Prompts tailored to specific time periods showed improved alignment with historical data
LLMs successfully replicated consumer preference patterns across demographic segments
Researchers explored using Large Language Models as substitutes for human survey participants in market research, focusing on perceptual analysis for brand positioning. The study demonstrated that LLMs can generate outputs closely matching human responses, potentially increasing efficiency by speeding up the research process and reducing costs. The researchers also showed how LLMs could tackle nuanced questions based on demographic variables or historical contexts that would be prohibitively expensive with human respondents.
AI tools assist students in summarizing complex materials and generating code snippets, freeing up classroom time for in-depth discussions and creative problem-solving.
Shifts focus from content delivery to higher-order thinking and analysis. [Source]
Teaching
Curriculum Evolution
Educational institutions are rapidly updating their marketing programs to include AI-related courses, ensuring students acquire skills increasingly demanded in the job market.
Prepares students for the evolving technological requirements of marketing careers.
Teaching
Ethical AI Discussions
Incorporating AI into education fosters discussions on responsible usage, guiding students to navigate the ethical implications of AI in professional marketing contexts.
Develops critical thinking about AI ethics alongside technical skills. [Source]
Innovative Assignment Approaches
Teaching
Draft Generation & Refinement
Professors require students to use AI tools like ChatGPT to generate initial drafts for marketing plans, which students then critique and refine, focusing on critical thinking over rote memorization.
Teaches critical analysis and strategic improvement of AI-generated content. [Source]
Teaching
Collaborative AI Engagement
Instructors and students collaboratively engage with AI during class to generate prompts, brainstorm project ideas, and refine research questions, learning the capabilities and limitations of AI tools.
Creates practical understanding of AI's strengths and weaknesses in marketing applications. [Source]
Teaching
Accessibility Enhancement
Utilizing AI in assignments helps level the playing field for non-native English speakers, allowing them to demonstrate strategic marketing thinking without language barriers.
Increases educational equity while maintaining focus on marketing strategy skills. [Source]
AI-Powered Student Projects
Teaching
Integrated Campaign Creation
Students create full marketing campaigns using AI tools—DALL·E for images, ChatGPT for copy, and AI video generators for advertisements—exploring AI's role in branding and originality.
Provides hands-on experience with AI tools across multiple marketing content formats. [Source]
Teaching
Reflective Learning Practice
At Northeastern University, students ran a multi-week AI experiment, keeping reflection logs to discuss AI's benefits and limitations in marketing applications.
Fosters metacognitive skills and critical evaluation of AI technologies. [Source]
Teaching
AI in Capstone Research
NYIT students studied AI's impact on marketing, including business adoption patterns and consumer perception of AI-generated advertisements, using surveys and experimental methods.
Prepares students to conduct research on emerging technologies in marketing contexts. [Source]
Agentic AI: The Next Wave
2025
What Is Agentic AI?
Agentic AI represents the next evolution beyond generative AI, featuring proactive intelligent agents that work through steps toward a goal. Based on robotic process automation principles, these AI agents can understand objectives, plan actions, and execute tasks with minimal human intervention.
Goal-oriented approach: Comprehends high-level goals and the AI agent's defined role
Multistep problem solving: Devises plans to reach objectives through logical steps
Self-directed execution: Takes tactical action, working with other tools, applications, and workflows
Adaptability: Flexibly handles trial-and-error and adapts to changing conditions
Source: Bain & Company, 2024
Agentic AI in Action
These videos demonstrate the capabilities and potential applications of agentic AI, showcasing how these autonomous agents can understand objectives, plan actions, and execute tasks with minimal human intervention.
Barriers to Adoption
• Limited generalization beyond narrow scopes
• Difficulty explaining AI decisions
• Challenges with undocumented workflows
• Poor cooperation among multiple agents
• Limited access to clean data and integrated tools
Organizations must address these barriers to successfully implement agentic AI solutions.
Workforce Impact
• Productivity increases as employees become AI supervisors
• Roles shift as agents democratize technical skills
• Decision-making cycles accelerate
• Human collaboration remains critical
• Risk management becomes more complex
Economic shifts will occur as agentic AI transforms job functions and organizational structures.
Effective Prompting Strategies
Prompting
Prompting for Complex Tasks
Following are steps to take when prompting for complex tasks
Not all steps are necessary for every task
Define the Objective:
Clearly state the main research question or task.
Specify the desired outcome (e.g., detailed analysis, comparison, recommendations).
Gather Context and Background:
Include all relevant background info, definitions, and data or documents.
Specify any boundaries (e.g., scope, timeframes, geographic limits).
Use Specific and Clear Language:
Provide precise wording and define key terms.
Avoid vague or ambiguous language.
Set a Role or Perspective:
Assign a specific role (e.g., "act as a market analyst" or "assume the perspective of a historian") to tailor the tone and depth of the analysis.
Provide Step-by-Step Guidance:
Break the task into sequential steps or sub-tasks.
Organize instructions using bullet points or numbered lists.
Specify Details of the Output Format:
Describe how the final answer should be organized (e.g., report format, headings, bullet points, citations).
Include any specific formatting requirements.
Balance Detail with Flexibility:
Offer enough detail to guide the response but allow room for creativity.
Avoid over-constraining the prompt to enable exploring relevant nuances.
Incorporate Iterative Refinement:
Build in a process to test the prompt and refine it based on initial outputs.
Allow for follow-up instructions to adjust or expand the response.
Solicit Questions for Clarifying Ambiguities:
Ask what is unclear about the prompt that needs to be clarified before proceeding—be sure to tell the model not to proceed until after clarifying.
Iteratively refine the prompt via recursively clarifying ambiguities.
Consider Your Task's Complexity:
Ask yourself, is your task too complex for one prompt?
If so, break the task up into multiple steps that can utilize their own prompts.
Apply Proven Techniques:
Use methods such as chain-of-thought prompting (e.g., "think step by step") for complex tasks.
Encourage the AI to break down problems into intermediate reasoning steps.
Avoid Overloading the Prompt:
Focus on one primary objective or break multiple questions into separate parts.
Prevent overwhelming the prompt with too many distinct questions.
Request Justification and References:
Instruct the AI to support its claims with evidence or to reference sources where possible.
Enhance the credibility and verifiability of the response.
Review and Edit Thoroughly:
Ensure the final prompt is clear, logically organized, and complete.
Remove any ambiguous or redundant instructions.
Prompting
Clear Context Setting
Define the AI's role clearly and specifically
Specify the desired output format
Provide relevant background information
State any constraints or requirements
Effective prompting begins with clear context setting, which significantly improves the quality and relevance of AI responses.
"As a market research analyst, evaluate {attached consumer behavior data 2020-2025} to identify emerging trends and purchasing pattern shifts.
Analyze how these trends correlate with changes in digital marketing strategies, particularly focusing on:
1) Social media platform algorithm changes
2) Influencer marketing effectiveness
3) Content format performance across demographics
Present your analysis with three data visualizations: a trend comparison, engagement matrix, and conversion forecast with 90% confidence intervals."
Prompting
Specific Instructions
Break down complex tasks into clear steps
Use clear, actionable language
Include example outputs when helpful
Specify evaluation criteria
Detailed instructions help ensure the AI's output aligns perfectly with your needs and requirements.
"As a digital marketing strategist, analyze {attached campaign performance data} and {current consumer sentiment indicators}.
Develop a comprehensive multi-channel marketing strategy that:
1) Identifies channels positioned for highest ROI in the next 2 quarters based on engagement metrics
2) Quantifies customer acquisition costs across platforms compared to industry benchmarks
3) Evaluates platform-specific algorithm changes and content format effectiveness
4) Recommends optimal budget allocation percentages with specific KPI targets
Include sensitivity analysis for three market scenarios: baseline, increased competition, and economic downturn."
Role-Based Prompting
Prompting
Marketing Research Role Prompting
Market Analyst: Identifying consumer trends and market opportunities
Brand Strategist: Developing positioning and messaging frameworks
Specifying marketing research roles helps frame the context and expertise level for AI responses.
"As a consumer insights specialist, analyze {attached customer survey data}, {social media sentiment reports}, and {purchase behavior statistics} to develop a comprehensive consumer profile.
Create a multi-dimensional segmentation model that:
1) Identifies key customer segments based on behavioral, psychographic, and demographic factors
2) Quantifies the impact of recent brand messaging on different segments' purchase intent
3) Maps customer journey touchpoints with engagement metrics by segment
4) Highlights potential conversion barriers at each funnel stage
5) Compares your findings with industry benchmarks from {attached market research reports}
Include statistical significance measures and clearly articulate your methodological approach."
Prompting
Marketing Strategy Role Prompting
Campaign Manager: Planning and executing marketing initiatives
Content Strategist: Developing content plans across channels
Digital Marketing Specialist: Optimizing online marketing efforts
Using specific marketing strategy roles helps focus the AI on relevant domain expertise and methodologies.
"As a digital marketing specialist, conduct a comprehensive channel audit on {attached marketing performance data} under current market conditions.
Your analysis should:
1) Quantify channel effectiveness across different conversion metrics (CPC, CPL, CAC, ROAS)
2) Model attribution patterns during different customer journey phases using {historical campaign data}
3) Assess content performance under varying audience targeting parameters
4) Evaluate platform algorithm changes and their impact on organic reach
5) Simulate campaign performance under three budget allocation scenarios
6) Recommend specific optimization strategies with ROI projections for each marketing channel
Include funnel visualization and cohort analysis at key conversion stages."
Chain-of-Thought Prompting
Prompting
Problem Decomposition
Break down complex problems into manageable components
Identify key variables and relationships
Create logical sequences for problem-solving
Build step-by-step solution approaches
Chain-of-thought prompting helps tackle complex problems by breaking them down into logical steps.
"Let's analyze {attached customer journey model} using a systematic framework:
1) Examine foundational assumptions regarding consumer behavior, touchpoint effectiveness, and decision-making processes
2) Identify all conversion factors and external influences with their relationship to purchase intent
3) Evaluate engagement patterns and loyalty indicators under various marketing scenarios
4) Assess attribution methodology against actual conversion data
5) Compare predicted customer responses with {attached A/B testing results}
6) Evaluate predictive accuracy using {historical campaign performance data}
7) Recommend specific modifications to improve model alignment with current consumer trends
For each step, provide data-driven insights and practical implementation strategies."
Prompting
Solution Validation
Verify logical consistency in each step
Check against original requirements
Test edge cases and assumptions
Review implications of proposed solutions
Systematic validation ensures solutions are robust and meet all requirements.
"Validate {attached marketing ROI model} for a multi-channel campaign strategy using the following comprehensive framework:
1) Evaluate attribution methodology, including touchpoint weighting and conversion path analysis
2) Audit engagement metrics against industry benchmarks and historical performance
3) Assess customer acquisition cost assumptions with detailed channel efficiency analysis
4) Review content performance and creative effectiveness relative to audience segments
5) Critique lifetime value calculations, including retention rate and repeat purchase patterns
6) Perform multi-variable testing analysis with audience segment considerations
7) Compare performance metrics with {comparable campaign results}
8) Identify specific optimizations needed to improve campaign effectiveness and efficiency"
Advanced Prompting Strategies
Prompting
Few-Shot Learning Approach
Provide examples of desired input-output pairs
Demonstrate patterns through multiple examples
Include edge cases and special scenarios
Show variations in acceptable outputs
Using examples helps the AI understand exactly what kind of output you're looking for.
"I've provided {three exemplar marketing campaign reports} that follow best practices in campaign performance reporting. Using these as reference models, transform {attached campaign analytics data} into a standardized format optimized for comparative analysis.
Your restructured marketing report should include:
1) Reformatted engagement metrics with consistent attribution methodology
2) Normalized conversion data with standardized funnel stage definitions
3) Comprehensive channel performance analysis with cross-platform comparisons
4) Detailed audience segment reporting with consistent metrics across demographics
5) Six-month trend analysis with period-over-period growth rates
6) Adjusted performance metrics that exclude seasonal anomalies
7) ROI analysis with industry benchmark comparisons
8) Creative performance insights organized by format and messaging type
Maintain all tracking parameter integrity while enhancing analytical utility."
Prompting
Best Practice
Output Formatting
Specify exact output structure needed
Define templates and formats
Include required metadata
Set clear style guidelines
Clear output specifications ensure responses are immediately usable without reformatting.
"Generate a comprehensive marketing campaign strategy for {proposed product launch} following this structured analytical framework:
Section 1: Current Market Landscape
• Detailed assessment of key market indicators and trends
• Competitive positioning analysis with market share evaluation
• Target audience segmentation with psychographic profiles
• Existing brand perception and market constraints
Section 2: Campaign Strategy Mechanics
• Detailed breakdown of messaging pillars and creative direction
• Channel selection rationale and touchpoint mapping
• Budget allocation methodology across channels
• Competitive benchmark comparison with similar campaigns
Section 3: Performance Projections
• Short-term engagement metrics by channel and audience segment
• Medium-term conversion implications with confidence intervals
• Long-term brand equity impacts on customer lifetime value
• ROI analysis under various market response scenarios
Section 4: Audience Impact Analysis
• Demographic response assessment across segments
• Geographic distribution of engagement and conversion
• Customer journey stage effectiveness
• Channel preference patterns by audience segment
Section 5: Risk Assessment
• Implementation risks with mitigation strategies
• Market saturation and competitive response scenarios
• Message fatigue and creative limitations
• Unintended brand association analysis
Section 6: Alternative Approach Comparison
• Quantitative comparison with alternative campaign concepts
• Efficiency and brand alignment tradeoff analysis
• Timing and seasonal considerations
• Complementary marketing initiatives recommendations
Section 7: Implementation Roadmap
• Detailed campaign timeline with critical path analysis
• Team responsibilities and agency coordination requirements
• Performance tracking framework with key performance indicators
• Adaptive optimization strategy for campaign adjustments
Use {attached market research data} and {competitive campaign benchmarks} to support your analysis throughout each section."
A visual research engine that creates interactive mind maps from academic papers and research materials. Features include connection identification between papers and concept visualization.
Enables visual exploration of research connections
An academic search engine powered by AI that searches over 200M research papers. Uses language models and vector search to surface relevant papers, synthesize insights, and provide evidence-based answers to research questions.
Delivers research-backed answers with direct links to source papers
Converts various content formats (YouTube, PDFs, documents, URLs, emails, recordings) into structured mind maps using advanced language models. Supports multiple LLM backends including GPT-4 and Claude.
A document editing platform that specializes in transforming text into visual content. The platform offers AI-powered tools that convert written content into graphics, diagrams, and video snippets, enhancing communication and idea clarity.
Transforms text into visual elements for enhanced communication
A research assistant platform that processes academic papers and research documents to provide verified, source-based answers to specific queries. Includes comprehensive summarization capabilities.
Facilitates source-verified research inquiry
Hands-on Prompting Exercise
Beginner
Research Paper Analysis
Learn to extract key information from academic papers using structured prompting with PDF documents.
⏱️
Duration: 20 mins
👥
Format: Individual
📄
File: PDF/Word
Exercise Steps:
1. Upload your research paper
2. Extract key sections
3. Analyze methodology
4. Identify research gaps
5. Generate future research directions
"Analyze this research paper and provide: 1) Key findings summary, 2) Methodology assessment, 3) Research gaps, 4) Potential future research directions. Format the output as a structured report with clear sections..."
Intermediate
Course Material Enhancement
Transform existing lecture notes and syllabi into enhanced learning materials with AI assistance.
"Review these papers and create: 1) A comparison matrix of methodologies and findings, 2) Thematic analysis, 3) Research gap identification, 4) Synthesis of key contributions to the field..."
Intermediate
Grant Proposal Enhancement
Improve grant proposals with AI-assisted analysis and recommendations.
"Review this grant proposal and provide: 1) Impact strengthening suggestions, 2) Methodology improvements, 3) Budget optimization recommendations, 4) Timeline refinements, focusing on NSF/NIH standard criteria..."
Advanced
Semantic Search Setup
Create a semantic search system for your research papers and documents.
"Help me create semantic search queries for my research papers that can: 1) Find related methodologies, 2) Identify similar findings, 3) Discover research gaps, 4) Connect related works across different papers..."
Wrap-up and Next Steps
Bridging Theory and Practice in the AI Era
Key Takeaways
Integration of AI across Marketing disciplines and functions
Practical prompting strategies for content creation and market analysis
Research opportunities in AI-enhanced consumer behavior modeling
Pedagogical approaches for Marketing education in the AI era
Next Steps
Implement AI tools in Marketing course materials and assignments
Develop domain-specific prompting guidelines for marketing content
