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An AI-and messaging-First Model For Student Experience

Updated: Oct 20, 2021

An AI-and messaging-First Model For Student Experience
AI-and messaging-First Model

Key Takeaways

  • The complexity and massive size of a university or a college can make navigating systems and services frustrating for students. They might choose to drop out in case the hurdles within its necessary processes restrict their progress.

  • Thoughtful implementation of artificial intelligence (AI) can ease the process of guiding students throughout their academic journey and make them self-capable of taking advantage of all the available resources.

  • Using conversational AI technology to manage interactions between a student and an institution will ensure student success and improve the professional lives of faculty members and other professionals at a campus.

Higher education institutes implement a wide range of strategies in order to enhance the student success rate. A few methods involve sophisticated analytics of data to identify students who need intervention. Some methods are applied to develop emotional and social resources for students, so they can meet their educational objectives.

Challenges Of Fragmented Student Experience

As conversation platform designers, we often see the higher education sector overlooking the opportunities of “user experience” in student outreach and engagement. Students depend on institutional resources and services to make smart academic, lifestyle, career, and financial choices. But most students find institutional resources tangled in a maze-like structure without any navigation assistance present.

The low quality of student experience lies between the fragmented and broken models.

In our AI partnership experience, we have found that institutions wish to assist students on an individual level. They want to guide every student on an enriching and fulfilling journey towards desired outcomes. Artificial intelligence has the potential to empower both students and institutions with the ease of support and engagement. Thoughtful AI implementation will amplify the abilities of faculty members and advisors to effectively help students.

Here’s how a well-connected network of resources looks like with an AI-first model:

AI First Model for student Outreach
AI First Model for Student Outreach

So, where is that dynamic AI solution to understand every student’s intent and assist with the right resources whenever they need?

The answer lies at ORAI. Our AI-first model for student experience has helped many of the renowned higher education institutions. We have designed an AI-powered Performance Platform to centralize every aspect of student experience and Student engagement:

AI Enabled University a Student Central Strategy
AI Enabled University
  • Pre-academia

  • During academia

  • Post-academia

An AI bot able to answer FAQs and provide admission resources to students instantly 24*7.

ORAI’s AI chatbot helping students through the whole admission process
ORAI’s AI Chatbot Can Help Students Through The Whole Admission Process

So, how can you deploy technology like ORAI’s across all your university's offerings? Would any generic chatbot interface be enough?

While a generic chatbot can answer only limited questions, AI-based conversational interfaces can understand the free-form of texts coming from humans to flexibly change their responses. So, you need to go beyond chatbot.

Merging Conversational AI With Traditional UI

For a college or university to provide a genuinely context-driven student experience, we believe an AI-first student interaction model should be merged with traditional interfaces:

1. Contextual Conversations

Most interactions begin with an initial conversation, which allows the system to identify a student’s unique intent and preferences. Blending a conversational system with traditional lists, images and buttons can help to add more context to this initial conversation. Students can easily navigate actions prompted through traditional elements and ask specific questions as well on the same platform.

Contextual conversations for immediate actions
Contextual Conversations For Immediate Actions

2. Actionable Transitions

A conversational AI merged with traditional UI can lead every conversation to a specific task. From there, a service transition can bring out a new set of capabilities and actions with the help of keyboard conventions.

ORAI’s AI chatbot  helping Actionable service transitions
Actionable service transitions

3. In-Chat Applets

For certain more complex actions, conversational AI can contextually represent inline applets. The applet appearance will be consistent with the conversational flow to help a student perform desired tasks.

What Would Your AI System Require?

In order to build your AI-first model for student experience, you would require better technological capabilities than what’s currently available at your campus.

To help you out, here are 4 conceptual pieces discussed in detail: intent identification, smart routing, and using existing data along with predictive models.

1. Intent Identification

NLP or Natural Language Processing is leveraged as technology to identify every student’s intent. This is then structured in the form of scripts, modeled responses or actions with an AI-based conversational system.

Giving a conversational flow is extremely important to keep a student engaged. Outdated bots give canned questions, which ends up looking more like a form than a two-way conversation.

Chatbot providing a conversational flow to the forms to keep a student engaged
Do you see the difference?!

A better approach to parse student intent is by listening to parameters and intent together within a conversational flow. For instance, if a student says, “I want two books from the library” or “I want books on Quantum Physics and Organic Chemistry”, both statements should trigger the same ‘Get a Book’ action. Also, the AI bot should include ‘Quantum Physics’ and ‘Organic Chemistry’ as two parameters for the required action for the second statement.

Hence, the bot should respond to fill the rest of the missing parameters: “I’ll arrange Quantum Physics and Organic Chemistry for you. For how long you’ll be keeping it?”

 NLP through intent, parameters, and actions
NLP through intent, parameters, and actions

2. Smart Routing

After mapping your student’s intent towards action, the AI system should be able to give a resource for the completion of that action. Or, it can show the result of a previous action too. Sometimes, it might have to ask another question like mentioned above to gather any missing parameter. Other times, it would simply give confirmation of action completion. In some cases, the bot might have to initiate another complex action with the help of an in-chat applet.

Smart routing is also for those intents that can’t be executed within the system. The bot should be smart enough to contextually help a user find the right resource for assistance for such queries.

Smart Routing from chatbot to human using 3-way communication
Smart Routing

An excellent property of ORAI’s Conversational AI Platform is its ability to easily integrate with your institution’s existing infrastructure. It serves as a connecting bridge between isolated resources and services. In simple words, it helps you maximize the benefits of what your infrastructure already has.

Similarly, we also believe that our conversational AI can be implemented in a phased model to easily connect all the existing resources and establish coordination between them. Hence, you can have an integrated system with all the required resources in one place.

ORAI’s Conversational AI Platform  ability to easily integrate with your institution’s existing infrastructure
One virtual assistant, different names

3. Existing Data

Identifying intent in real-time only completes half of the job. A contextually smart conversational platform should also be able to use existing data related to the student body, individual students, and all other sources. This can help the bot figure out the current stage of a student’s college journey and offer the right information and functionality. For instance, a bot can provide payment information for students, or schedule orientation for a student who is in his/her first week in college.

 The virtual assistant knows it’s Becky’s first week of college
The virtual assistant knows it’s Becky’s first week of college

4. Predictive Models

Existing data can also enable predictive models regarding campus activities, student interests, and other things. Such models play an important role in offering recommendations, mapping specific intents, and creating personalized content to ensure highly relevant services, resources, and actions.

Predictive models are also necessary for times when the artificial intelligence chatbot has to apply the changes that occur over time – new predictive data results in new personalized responses, new recommendations, and new patterns of routing.

Final Thoughts

We aim to enable this AI-first model of student experience for all futuristic universities and colleges to enable contextual awareness, fluid interactions, and personalized engagement.

If you wish to build something like this for your higher-ed institution, let’s have a quick talk. We can give you a proper demo as well.

Swapnil Jain

CEO-ORAI Robotics


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