A 501(c)(3) nonprofit organization dedicated to harnessing the power of artificial intelligence (AI) for social good. Our organization focuses on ethical AI, mentorship, transparency, and ensuring AI benefits underserved communities.
- Fellows Program: Mentorship and support for emerging AI researchers, practitioners, and community leaders to develop responsible AI solutions that address pressing social challenges.
- AI Research: Conducting and supporting projects aimed at promoting transparency, fairness, and accountability in AI systems.
- AI for Good Software: Developing and deploying AI-powered tools for real-world social impact with an emphasis on inclusivity and accessibility.
- Entrepreneurship: Fostering innovation and supporting AI startups focused on social impact and ethical technology development.
These AI-powered books guide conversations dynamically, adapting in real-time based on the user's background, learning style, and goals. Professors can integrate their teaching materials while students benefit from tailored learning paths, instant feedback, and interactive content.
We're building a comprehensive library of tools enabling AI systems to take purposeful actions following a four-step process:
- Perceive – Gather and process data from various sources
- Reason – Use Large Language Models to generate solutions
- Act – Execute tasks through API integrations with built-in guardrails
- Learn – Continuously improve through feedback loops
- Ada – Chatbot for Introductory Calculus providing step-by-step guidance
- Newton – Chatbot for Physics with dynamic graphs and simulations
- Grace – Chatbot for Algorithms using interactive visualizations
- Playfair – Intelligent Course Assistant for Northeastern's INFO 7390
- PredictaBio – AI-driven synthetic protein synthesis
- The RAMAN Effect Project – AI-powered public health monitoring using Raman Spectroscopy
- Alfalfa.AI – AI-driven support for small cannabis farmers using blockchain technology
- Cognitive Type Project – AI-generated fonts for improved readability and accessibility
- Synthetic Personas – AI-driven research personas for survey and market research
- The Shannon Project (ClaudeNEU) – Open-source AI tools for education leveraging Claude Enterprise and LLMs
- Skills Mapping: AI4ED Lex – Bridging job market demands with education through NLP and machine learning
- NanoLex – A lexical database for nanomedicine NLP & LLM training
- INFO 7375: Computational Skepticism and AI – Advanced course on AI validation and ethics
- Project Tapestry – Open-source platform connecting job seekers with suitable positions
- Lyrical Literacy – AI-powered cognitive and language development through music
- Cat Connection (CatBot) – AI-powered chatbot enhancing pet adoption experiences
Our post-graduate transitional volunteer fellowship bridges the gap between graduation and employment. The program is OPT eligible for international students and provides hands-on, project-based learning to enhance AI skills and contribute to impactful projects.
Join the conversation, provide feedback, or let us know how Humanitarians AI could benefit you and your community.
Join us in our mission to use AI for the greater good. Your support helps us develop ethical AI solutions that address real-world challenges in education, healthcare, nonprofits, and the arts.
- Volunteer/Mentor: Share your expertise and help guide the next generation of AI researchers
- Donate: Support our projects financially or in-kind
- Subscribe: Stay updated on our latest projects and opportunities
đź”— Learn more about our work:
Humanitarians AI GitHub
A comprehensive suite of educational AI chatbots and tools designed to make AI accessible to everyone. Named after educational philosopher John Dewey, these tools provide personalized learning experiences across various disciplines.
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Ada (Calculus Bot): Provides step-by-step guidance, Socratic questioning, and visualization for calculus concepts, helping students understand complex mathematical principles without giving direct answers.
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Newton (Physics Bot): Enhances physics learning through dynamic graphs and interactive simulations, offering guided explanations tailored to course materials.
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Grace (Algorithms Bot): Assists computer science students with algorithm understanding through simulations and interactive visualizations, improving problem-solving skills.
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Playfair: An intelligent course assistant for data science courses, offering structured explanations with visualization tools and real-world applications of theoretical concepts.
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Archimedes: Simplifies structural engineering concepts through clear explanations, real-world examples, and interactive problem-solving.
Named in honor of Lawrence Dunbar Reddick (1910-1995), the pioneering historian who documented misrepresentations of Black history in textbooks, the Reddick Project uses AI to create historically accurate visual representations of underrepresented or misrepresented events and figures in history.
The Reddick Project leverages generative AI to produce educational materials that address historical gaps and misrepresentations identified through rigorous historical research. By creating AI-generated images, videos, and interactive media that visualize historically accurate but previously undocumented moments, the project helps students and educators engage with marginalized historical narratives.
- Historical Recovery Studio: AI-powered reconstruction of historical events and figures with minimal visual documentation, based on written primary sources, oral histories, and scholarly research.
- Counterfactual Visualization Tool: Creates clearly labeled "what if" scenarios showing how historical events might have been depicted if documented with the same attention given to mainstream historical figures and events.
- Textbook Correction Engine: Analyzes existing educational materials to identify misrepresentations and generates alternative visual content that more accurately reflects historical realities.
- Archive Expansion Initiative: Collaborates with libraries, museums, and historical societies to supplement their collections with AI visualizations based on their textual archives.
- Implements ethical guardrails ensuring all generated content is:
- Clearly labeled as AI-generated
- Historically validated by subject matter experts
- Accompanied by primary sources and historical context
- Designed to combat rather than reinforce stereotypes
- Provides educators with visual resources for teaching histories that lack photographic documentation
- Creates immersive learning experiences about historical periods and communities that have been systematically excluded from visual records
- Helps students visualize the contributions of underrepresented groups throughout history
- Offers specialized modules focusing on Indigenous histories, women's contributions, labor movements, disability history, and immigrant experiences
- Stonewall Uprising: Visual documentation of the 1969 Stonewall riots that catalyzed the LGBTQ+ rights movement, recreating scenes based on firsthand accounts and archival research.
- Crispus Attucks: Interactive narrative exploring the life and legacy of Crispus Attucks, the first person killed in the Boston Massacre and an early martyr of the American Revolution.
- Overlooked Nerds: Series highlighting historically accurate portrayals of scientific pioneers like Ada Lovelace and Isaac Newton, contextualizing their work and personal lives beyond simplified historical accounts.
- Mythological Recontextualization: Culturally authentic visualizations of global mythological traditions including Grimm's fairy tales and The Ramayana, presenting these stories with their original cultural contexts and meanings.
An initiative dedicated to revolutionizing education through open-source AI tools, leveraging Claude Enterprise and other LLMs to create accessible, customizable solutions.
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Key Components: 12-step framework for AI education integration, no-code chatbot development tools, and vector databases for knowledge retrieval.
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Practical Applications: Virtual office hours assistants, academic writing support, lecture enhancement, multilingual quiz translation, and AI-powered grading.
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Advanced Features: Text-to-video generation, AI-generated educational images, Zoom lecture integration, and personalized study guides.
An advanced course focusing on developing critical thinking, data validation, and AI model assessment skills.
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Key Themes: Philosophical skepticism in AI, explainable AI techniques, bias detection, and AI model transparency.
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Course Structure: Combines philosophy, logic, and technical validation methods to ensure students can critically evaluate AI systems.
A research-based program exploring the intersection of neuroscience, music, and education to enhance cognitive and language development through AI-generated musical content.
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Core Concept: Uses music to engage multiple brain regions simultaneously, creating customized songs and interactive content to target cognitive and linguistic skills.
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Applications: Early literacy support, multilingual education, special education tools, and brain health enhancement through music.
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Research Papers: Includes studies on the neuroscience of singing, music training's impact on mathematical cognition, and the relationship between music and second language acquisition.
An AI-powered customer support chatbot designed to enhance pet adoption experiences at animal shelters.
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Key Features: Adoption process guidance, personality matching algorithms, support for senior and special needs animals, and database integration.
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Benefits: Streamlines pet adoption process, reduces shelter workload, and helps more animals find suitable homes.
A data science research project analyzing what economic and social factors contribute to national happiness scores.
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Methodology: Combines data from the World Happiness Report with economic indicators, population data, political systems information, and other metrics.
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Technical Approach: Uses exploratory data analysis, predictive modeling, and model interpretability techniques to determine which factors most strongly influence happiness.
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Applications: Informs policy decisions, advances understanding of economic development's relationship to wellbeing, and identifies undervalued factors in societal satisfaction.
Conversational AI-driven learning platforms that personalize education through interactive dialogue, adapting in real-time based on the user's background, learning style, and goals.
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Key Innovations: Dynamic conversation guidance, integration with existing teaching materials, and custom learning paths.
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Implementation: Generates complete books from a Table of Contents, integrates multimedia for hands-on learning, and provides adaptive content.
A comprehensive library of tools enabling AI systems to take purposeful actions following a four-step process: perceive, reason, act, and learn.
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Capabilities: Enables AI systems to gather data, generate solutions, execute tasks through API integrations, and continuously improve through feedback.
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Applications: Customer service automation, content generation, and decision support across industries.
An open-source framework for personalized AI assistance in service businesses featuring knowledge base management, conversation engines, and analytics dashboards.
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Architecture: Modular design with six core components for business-specific information, customer interactions, and data collection.
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Benefits: Democratizes access to technology traditionally available only to large corporations, improving customer experiences while gathering business intelligence.
A framework for enhancing binary classification pipelines through intelligent automation and optimization, particularly for non-profit organizations.
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Key Components: Algorithm integration framework, AI-driven evaluation, automated leaderboard, and hyperparameter optimization.
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Applications: Donor retention prediction, program outcome forecasting, beneficiary targeting, and volunteer matching.
AI-driven solutions for synthetic protein synthesis that create "recipes" for novel proteins with specific properties.
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Technology: Uses AI to accelerate discovery and enable custom protein design for applications in pharmaceuticals, scents, and bioengineering.
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Impact: Streamlines protein production, improves efficiency, and reduces research costs in biotechnology.
Combines Surface-Enhanced Raman Spectroscopy (SERS) with AI to enhance pathogen and pollutant detection in wastewater epidemiology.
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Methodology: Uses deep learning algorithms to analyze spectral data from wastewater samples, enabling early detection of disease outbreaks and environmental threats.
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Applications: COVID-19 surveillance, antimicrobial resistance tracking, substance use monitoring, and environmental pollutant detection.
AI-enhanced typography that creates fonts with specific cognitive benefits through generative AI and cognitive science.
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Key Innovations: "Text to Type" AI models that optimize fonts for readability, engagement, and accessibility.
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Applications: Improving online ad engagement, enhancing children's reading experiences, and supporting individuals with dyslexia.
AI-generated research personas that enhance survey research, user testing, and communication strategies.
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Technical Approach: Integrates Big Five personality traits and demographic data to generate realistic, behaviorally diverse personas.
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Tools: Includes SurveyMind, MarketMind, BrandEcho, and other frameworks for generating and analyzing synthetic respondents.
Investigates how extracellular vesicles can be used in anti-aging therapies and regenerative medicine using AI analysis of molecular cargo.
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Scientific Focus: Studies biogenesis, anti-aging mechanisms, therapeutic applications, and delivery systems for exosomes.
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AI Integration: Uses machine learning for molecular cargo analysis, pathway modeling, and treatment optimization.
Research on how organizations can implement AI technologies responsibly while minimizing environmental impact.
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Key Areas: Energy consumption analysis, resource utilization, carbon footprint measurement, and environmental impact reduction strategies.
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Strategic Recommendations: Covers energy-efficient AI architectures, optimized hardware, renewable energy integration, and corporate accountability.
Educational resources for teaching causal inference methodologies through AI-enhanced tutorials and tools.
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Curriculum: Covers foundational concepts, potential outcomes framework, randomization principles, estimation methods, and advanced techniques.
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Tools: Interactive tutorials, code generators, visual explainers, and case study builders for causal analysis.
Evaluation framework for comparing, interpreting, and building trust in Large Language Models.
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Evaluation Dimensions: Performance metrics, learning capabilities, robustness assessment, and fairness evaluation.
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Novel Tools: LLMMaps for domain-specific assessment, Bloom's Taxonomy visualization, Hallucination Score quantification, and Knowledge Stratification.
Research on how AI-powered shopping agents are transforming advertising, requiring new approaches to targeting and brand engagement.
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Key Findings: Documents the shift from emotional to algorithmic marketing, the rise of machine-optimized ad formats, and the decline of impulse purchasing.
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Applications: AI-friendly content strategies, pricing algorithm design, and brand differentiation techniques for an AI-driven marketplace.
Explores how AI can create dynamic, personalized narratives that adapt to audience choices, emotions, and behaviors.
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Technologies: Implements language models for dialogue, sentiment analysis for emotional adaptation, and procedural generation for evolving worlds.
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Applications: Entertainment, education, healthcare, training, and marketing through AI-responsive storytelling systems.
An experiential AI playground at Northeastern University that connects experienced AI practitioners with novices for mentorship and open-source development.
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Core Principles: Experiential learning, mentorship, open-source contribution, community building, and portfolio development.
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Initiatives: Research papers, technical articles, open-source projects, and specialized applications in computational art, biology, finance, and typography.
AI-powered case interview preparation for students and professionals entering management consulting and related fields.
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Key Components: Interactive case simulations, structured methodology training, feedback systems, and interview skill development.
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Methodological Framework: Teaches a systematic approach to case interviews, from initial engagement through problem analysis.
Developing code and tutorials to help students and non-profits create branded websites for personal and organizational marketing.
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Project Focus: Leverages Vercel's V0 technology to transform text descriptions into functional website components.
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Educational Approach: Follows the "Maya and V0" framework of iterative development (just type, then check, then iterate).
A collaborative tool for ethical data collection from social media for research purposes.
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Features: Comprehensive data collection, AI-powered image captioning, responsible data handling, and privacy-conscious design.
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Applications: Media studies research, AI training dataset creation, digital marketing education, and information science.
AI-driven support for small cannabis farmers, helping them navigate legal complexities and secure capital through blockchain technology.
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Key Components: Blockchain-powered cannabis futures trading, real-time crop optimization, and regulatory compliance guidance.
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Economic Impact: Enables rural farmers to participate in a high-value crop market, with potential yields exceeding traditional agriculture.
An educational publishing project that transforms student learning into professional authorship while creating resources for data practitioners.
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Content: Covers data cleaning, feature engineering, missing value handling, and other essential data preparation techniques.
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Implementation: Uses Python tools including Pandas, Scikit-learn, and visualization libraries to demonstrate practical applications.
An open-source lexical database mapping skills from job listings and educational curricula using NLP and machine learning.
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Methodologies: Employs discriminating terms analysis, semantic enrichment, contextual classification, and statistical validation.
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Applications: Educational program enhancement, career pathway optimization, and labor market transparency.
A specialized lexical database for nanomedicine NLP and LLM training.
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Structure: Comprehensive entries with definitions, classifications, semantic relations, and application descriptions.
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Applications: Scientific communication enhancement, LLM training for specialized knowledge, and regulatory support.